This Page Intentionally Left Blank. NGEE Proposal... · Jitendra Kumar, Scott Painter, and Peter...

This Page Intentionally Left Blank.

Next-Generation Ecosystem Experiments - Arctic (NGEE Arctic)

Submitted to

J. Michael Kuperberg Program Manager

Department of Energy, Office of Science, Biological and Environmental Research

June 12, 2015

Prepared by

Stan Wullschleger, Tom Boden, David Graham, Baohua Gu, Les Hook, Colleen Iversen, Jitendra Kumar, Scott Painter, and Peter Thornton

Oak Ridge National Laboratory

Alistair Rogers, Alice Cialella, Keith Lewin, and Shawn Serbin Brookhaven National Laboratory

Susan Hubbard, Gautam Bisht, Baptiste Dafflon, Charlie Koven, John Peterson, William Riley, Neslihan Tas, Margaret Torn, and Haruko Wainwright

Lawrence Berkeley National Laboratory

Cathy Wilson, Ethan Coon, Dylan Harp, Jeff Heikoop, Satish Karra, Elizabeth Miller, Brent Newman, Joel Rowland, and Chonggang Xu

Los Alamos National Laboratory

Robert Bolton, Vladimir Romanovsky, Amy Breen, Robert Busey, Bill Cable, Eugenie Euskirchen, Jessica Cherry, Alexander Kholodov, A. David McGuire, Dmitry Nicolsky, Santosh Kumar Panda, and

Jessie Young University of Alaska Fairbanks

Dave Billesbach University of Nebraska Lincoln

This Page Intentionally Left Blank

CONTENTS P a g e | vii

CONTENTS SIGNATURE PAGE ................................................................................................................................................... v

ABBREVIATED TERMS ........................................................................................................................................ ix

EXECUTIVE SUMMARY ..................................................................................................................................... xiii 1. ABSTRACT ........................................................................................................................................................ 1

2. BACKGROUND AND JUSTIFICATION ....................................................................................................... 3

3. VISION ............................................................................................................................................................... 7

4. PROGRESS AND ACCOMPLISHMENTS .................................................................................................... 9

Establishment of physical infrastructure ............................................................................................. 9

Multi-scale characterization of landscapes across the Barrow Peninsula ............................ 11

Integration of ModEx philosophy into modeling and scaling strategies ................................ 15

Data availability for project participants and the larger community ..................................... 20

Impact and relevance of Phase 1 research ....................................................................................... 20

5. Research Plan .............................................................................................................................................. 21

5.A Modeling and Scaling Strategy ................................................................................................... 21

5.B Site Selection and Sampling Strategy....................................................................................... 28

5.C Integrated Research Plans ........................................................................................................... 31 Question 1. How does the structure and organization of the landscape control the

storage and flux of carbon and nutrients in a changing climate? ....................... 31

Question 2. What will control rates of CO2 and CH4 fluxes across a range of permafrost conditions? ....................................................................................... 36

Question 3. How will warming and permafrost thaw affect above- and belowground plant functional traits, and what are the consequences for Arctic ecosystem carbon, energy, water, and nutrient fluxes? ...................................... 41

Question 4. How will shrub distributions change and generate climate feedbacks with expected climate warming in the 21st century? ......................................... 46

Question 5. Where, when, and why will the Arctic become wetter or drier? ........................ 51

6. MANAGEMENT PLAN AND TEAM INTEGRATION ............................................................................. 57

7. INFORMATION/DATA MANAGEMENT AND COMMUNITY OUTREACH .................................... 63

8. KEY PERSONNEL ......................................................................................................................................... 67

9. USE OF DOE FACILITIES AND CAPABILITIES IN SUPPORT OF THE NGEE ARCTIC PROJECT ........................................................................................................................................................ 69

10. PROPOSED COLLABORATORS................................................................................................................ 73

11. BIBLIOGRAPHY ........................................................................................................................................... 93

12. BUDGET ....................................................................................................................................................... 109

13. BUDGET JUSTIFICATION ........................................................................................................................ 125

CONTENTS P a g e | viii

14. CURRICULUM VITAE ................................................................................................................................ 145

APPENDIX ............................................................................................................................................................. A-1

Project Publications ................................................................................................................................. A-3

Project Roles, Responsibilities, and Authorities ........................................................................... A-7

Data Management Plan ........................................................................................................................... A-9

Data Sharing Policy ............................................................................................................................... A-11

Model Sharing Policy ............................................................................................................................ A-17

Fair Use Policy ......................................................................................................................................... A-19

Selected Dataset DOIs and Citations ............................................................................................... A-21

Project Work Flow Diagram ............................................................................................................... A-23

ABREVIATED TERMS P a g e | ix

ABBREVIATED TERMS 1D one-dimensional 2D two-dimensional 3D three-dimensional ABoVE Arctic Boreal Vulnerability Experiment ACME Accelerated Climate Model for Energy AGU American Geophysical Union ALM ACME land model ALT active layer thickness APS Advanced Photon Source ARM Atmospheric Radiation Measurement ASR Atmospheric System Research ATS Arctic Terrestrial Simulator model or Advanced Terrestrial Simulator BEO Barrow Environmental Observatory BER Office of Biological and Environmental Research BNL Brookhaven National Laboratory CAMS Center for Accelerator Mass Spectrometry CARVE Carbon in Arctic Reservoirs Vulnerability Experiment CDIAC Carbon Dioxide Information Analysis Center CLM Community Land Model CS Chief Scientist DEM digital elevation model DGVM Dynamic Global Vegetation Model DM Data Manager DMT data management team DOC dissolved organic carbon DOE Department of Energy DOI Digital Object Identifier DOM dissolved organic matter ED Ecosystem demography EESD Energy and Environmental Sciences EL elevation EMSL Environmental Molecular Science Laboratory EOMI Experiments, observations, modeling, and other investigations ERT Electrical Resistance Tomography ESGF Earth System Grid Federation ESH&Q Environment, Safety, Health, and Quality ESM Earth System Model FACE Free-Air CO2 Enrichment FTP File Transfer Protocol

ABREVIATED TERMS P a g e | x

GHG Greenhouse gas GPP Gross and net primary productivity HPC High Performance Computing IDEAS Interoperable Design of Extreme Scale Application Software IL Institutional Lead ILAMB International Land Model Benchmarking [Project] IM Integrated Modeling IPCC Intergovernmental Panel on Climate Change JGI Joint Genome Institute Jmax maximum electron transport LAI leaf area index LANL Los Alamos National Laboratory LBNL Lawrence Berkeley National Laboratory LiDAR Light and Radar (or Light Detection and Ranging) LRD Laboratory research director ModEx Model-experiment integration MR model requirement NASA National Aeronautics and Space Administration NCAR National Center for Atmospheric Research NEE Net ecosystem exchange NEON National Ecological Observatory Network NetCDF Network Common Data Form NGEE Next-Generation Ecosystem Experiments NOAA National Oceanic and Atmospheric Administration NSA North Slope of Alaska NSF National Science Foundation OM organic matter OME Online Metadata Editor ORNL Oak Ridge National Laboratory PCN Permafrost Carbon Network PCN Permafrost Carbon Network PFLOTRAN A multiphase, multicomponent model of subsurface flow and reactive transport PFT Plant functional type PM Project Manager POC point of contact POC Point of contact POD Proper Orthogonal Decomposition PVC polyvinylchloride Q science question ROM Reduced order model SC Office of Science

ABREVIATED TERMS P a g e | xi

SFA Science Focus Area SOM Soil Organic Matter SPRUCE Spruce and Peatland Responses Under Climatic and Environmental Change Experiment SSRL Stanford Synchrotron Radiation Lightsource STL Science Team Leader TEM Terrestrial Ecosystem Model TES Terrestrial Ecosystems Sciences TL Task Leader TRF Temperature response functions TRY Plant trait database UAF University of Alaska Fairbanks UAS Unmanned aircraft systems UQ uncertainty quantification USGS United States Geological Survey Vc, max maximum rate of CO2 carboxylation VD variance decomposition WBS Work Breakdown Structure XAS X-ray absorption spectroscopy ZPW zero power warming

ABREVIATED TERMS P a g e | xii


EXECUTIVE SUMMARY P a g e | xiii

EXECUTIVE SUMMARY An important challenge for Earth System Models (ESMs) is to represent land surface and subsurface processes and their interactions in a warming climate. This is true for all regions of the world, but it is especially relevant in the Arctic where surface air temperatures at high latitudes are projected to warm at a rate twice that of the global average in the coming century. While changes in regional temperatures are expected to impact sea ice, snowpack, permafrost, and other components of the Arctic system, these changes are made even more important because they are expected to play, and may already be playing, a role in determining the climate of the rest of the globe.

The Next-Generation Ecosystem Experiments (NGEE Arctic) is a 10-year project (2012 to 2022) to reduce uncertainty in ESMs through developing a predictive understanding of carbon-rich Arctic system processes and feedbacks to climate. This is achieved through experiments, observations, and synthesis of existing datasets that strategically inform model process representation and parameterization and enhance the knowledge base required for model initialization, calibration, and evaluation. The concept of model-experiment integration (ModEx) requires strong collaboration between scientists developing and testing models and those conducting research in the field and laboratory. Motivated by a ModEx approach, this proposal highlights progress made by the multi-disciplinary NGEE Arctic team in Phase 1 and describes plans for Phase 2. In Phase 1 (2012 to 2014), NGEE Arctic tested and applied a multi-scale measurement and modeling framework in coastal tundra on the North Slope of Alaska. Field plots, transects, and satellite sites near Barrow, Alaska, were chosen to represent a cold, continuous permafrost region at the northern extent of an ecological and climatic gradient. Much of our research focused on subgrid heterogeneity in thermal-hydrology, biogeochemistry, and vegetation structured by topography, landscape, and drainage networks. These efforts provided datasets and derived products and knowledge that meet project requirements for model initialization, parameterization, process representation, and evaluation. Some of these capabilities are now being adopted by DOE’s Earth System Modeling program as fundamental new developments in a next-generation ESM, the Accelerated Climate Model for Energy (ACME).

Building upon research conducted in the first three years of the project, in Phase 2 (2015 to 2018) we will pursue additional field, laboratory, and modeling objectives in Barrow, Alaska. This research will proceed along a natural line of investigation that takes advantage of ongoing data collection, knowledge discovery, and model development. We also propose to establish a southern site which, compared to our research site on the North Slope, is characterized by transitional ecosystems, warm, discontinuous permafrost, higher annual precipitation, and well-defined watersheds with strong topographic gradients. Our selection of the Seward Peninsula is based on a Phase 1 analysis indicating that western Alaska is a proxy for the future ecological and climatic regime of the North Slope of Alaska toward the end of the century. Expanding our activities to the Seward Peninsula will allow us to challenge our Phase 1 scaling strategy with a contrasting environment that will require new process understanding and representation in models. We will use variation in the structure and organization of the Seward Peninsula landscape to guide a series of process-level investigations (Questions 1 through 3) that will be nested at scales ranging from core to plot, landscape, and watershed levels. Knowledge derived in these studies will identify mechanisms controlling carbon, water, nutrient, and energy fluxes, which will then be brought to bear on two integrative questions concerning the future of the Arctic in a changing climate (Questions 4 and 5).

Q1. How does the structure and organization of the landscape control the storage and flux of carbon and nutrients in a changing climate?

Q2. What will control rates of CO2 and CH4 fluxes across a range of permafrost conditions? Q3. How will warming and permafrost thaw affect above- and belowground plant functional traits,

and what are the consequences for Arctic ecosystem carbon, water, and nutrient fluxes? Q4. What controls the current distribution of Arctic shrubs, and how will shrub distributions and

associated climate feedbacks shift with expected warming in the 21st century?

EXECUTIVE SUMMARY P a g e | xiv

Q5. Where, when, and why will the Arctic become wetter or drier, and what are the implications for climate forcing?

Our model-inspired vision implemented in Phase 1, and now extended into Phase 2, strengthens the connection between process studies in Arctic ecosystems and high-resolution scaling strategies that form the foundation of DOE’s land surface modeling for climate prediction. The NGEE Arctic project supports the BER mission to advance a robust predictive understanding of Earth’s climate and environmental systems by delivering a process-rich ecosystem model, extending from bedrock to the top of the vegetative canopy/atmospheric interface, in which the evolution of Arctic ecosystems in a changing climate can be modeled at the scale of a high-resolution, next-generation ESM grid cell. Implicit in our expanded scope of research in Phase 2 is the need to build upon the scaling and modeling framework established in Phase 1 and to populate that framework with knowledge derived from experiments and observations from new and existing sites. This will facilitate upscaling our field and landscape-scale observations to regional scales, and encourage continued interactions on the North Slope of Alaska with the DOE’s ARM and Atmospheric System Research (ASR) programs and cross-agency collaborations with NSF (NEON), NOAA, USGS, and NASA through their CARVE and ABoVE campaigns. The new ASCR-BER project Interoperable Design of Extreme-scale Application Software (IDEAS) is using an NGEE Arctic thermal hydrology model to simulate polygonal tundra at the Barrow field site as one of its two use cases. The IDEAS Arctic use case will focus on refactoring of the software developed in Phase 1 to extend the current thermal hydrology capability to much larger spatial regions. Our research task on plant traits and trait-enabled modeling (i.e., Q3) is a direction that is consistent with that of the NGEE Tropics and ACME projects and represents an area where close collaboration among our projects will be encouraged. We will continue to collaborate with the TES SFA at Argonne National Laboratory as together we share knowledge and samples that can be used by the SFA to develop regional maps of soil carbon stocks and their intrinsic decomposability for model benchmarking. We will coordinate with the SPRUCE effort within the TES SFA at Oak Ridge National Laboratory to make use of new sub-grid models of wetland hydrology and microtopography.

In Phase 3 (2019 to 2022), we expect to be in a strong position to conduct pan-Arctic simulations using a model with unparalleled sophistication in its cross-scale process representation that is parameterized and evaluated against a multi-scale, nested hierarchy of measurements and synthesis products. Integration and a truly interdisciplinary perspective, forged by our team in Phase 1, will be foundational to Phase 2 activities and beyond as we use model sensitivity and uncertainty analysis and new process knowledge to guide computational, experimental, and observational efforts toward improved climate predictions in high-latitude ecosystems. Safety, collaboration, communication and outreach, and a strong commitment to data management, sharing, and archiving are key underpinnings of our model-inspired research in the Arctic.

ABSTRACT P a g e | 1

1. ABSTRACT An important challenge for Earth System Models (ESMs) is to accurately represent land surface and subsurface processes and their complex interactions in a warming climate. This is true for all regions of the world, but it is especially important for high-latitude Arctic ecosystems which are projected to warm at a rate twice that of the global average by the end of the 21st century. The Next-Generation Ecosystem Experiments (NGEE Arctic) is a 10-year project (2012 to 2022) that seeks to increase our confidence in global climate projections through a coordinated series of model-inspired investigations undertaken by a collaborative team of modelers, data managers, and empiricists spanning a range of scientific disciplines. NGEE Arctic focuses on high-latitude ecosystems underlain by carbon-rich permafrost that are vulnerable to thaw in a warmer climate. In Phase 1 (2012 to 2014), NGEE Arctic tested and applied a multi-scale measurement and modeling framework for ecosystems and watersheds characterized by cold, continuous permafrost on the North Slope of Alaska. These efforts provided unique datasets for model parameterization and benchmarking and knowledge on topics ranging from watershed hydrology to plant physiology that is being adopted by DOE’s Earth System Modeling program as fundamental new developments in a next-generation ESM, the Accelerated Climate Model for Energy (ACME). In Phase 2 (2015 to 2018), we propose to establish a southern site which, compared to our research site on the North Slope, is characterized by transitional ecosystems; warm, discontinuous permafrost; higher annual precipitation; and well-defined watersheds with strong topographic gradients. Our selection of the Seward Peninsula is based on a Phase 1 analysis indicating that western Alaska is a proxy for the future ecological and climatic regime of the North Slope of Alaska toward the end of the century. One or more sites on the Seward Peninsula provide an opportunity to expand our understanding and model representation of (1) landscape structure and organization on the storage and flux of carbon, water, and nutrients, (2) edaphic and geochemical mechanisms responsible for variable CO2 and CH4 fluxes across a range of permafrost conditions, (3) variation in plant functional traits across space and time, and in response to changing environmental conditions, (4) controls on shrub distribution and associated climate biogeochemical and biophysical feedbacks, and (5) changes in surface and groundwater hydrology expected with warming in the 21st century. Our vision in Phase 1, and now extended into Phase 2, strengthens the connection between process studies in Arctic ecosystems and high-resolution landscape modeling and scaling strategies that will foster a strong interaction across the DOE Biological and Environmental Research (BER) program. The NGEE Arctic project supports the BER mission to advance a robust predictive understanding of Earth’s climate and environmental systems by delivering a process-rich ecosystem model, extending from bedrock to the top of the vegetative canopy/atmospheric interface, in which the evolution of Arctic ecosystems in a changing climate can be modeled at the scale of a high-resolution, next-generation ESM grid cell. Research in Phase 1, and now proposed for Phase 2, prepares our team for pan-Arctic simulations of ecosystem-climate feedbacks in Phase 3 (2019 to 2022). Safety, collaboration, communication and outreach, and a strong commitment to data management, sharing, and archiving are key underpinnings of our model-inspired research in the Arctic.

ABSTRACT P a g e | 2


BACKGROUND AND JUSTIFICATION P a g e | 3

2. BACKGROUND AND JUSTIFICATION The Arctic may be the most climatically sensitive region on Earth. High latitudes have experienced the greatest regional warming in recent decades and are projected to warm at a rate twice that of the global average in the coming century (Bieniek et al. 2014). The implications of such warming include permafrost thaw and deepening of the active layer, microbial decomposition of vulnerable soil organic matter, altered productivity and migration of vegetation, and changes in surface and groundwater storage (Hinzman et al. 2013). Recent literature emphasizes that the following topics are central priorities for experimental, observational, and model development research:

• Improved representation of subgrid heterogeneity related to permafrost distribution, soil carbon stocks, surface inundation, distribution of vegetation, and atmospheric forcing (Rowland et al. 2010; Aleina et al. 2013);

• Better descriptions of permafrost thaw and deepening of the active layer, and the consequences for microbial dynamics and carbon feedbacks to climate (Cahoon et al. 2012; Hodgkins et al. 2014; McCalley et al. 2014; Schuur et al. 2015);

• Increased understanding of the fundamental controls on vegetation dynamics and their representation in models, including the relationships and tradeoffs among plant functional traits, above and below ground, that enable future innovations in modeling (Epstein et al. 2004a; Wookey et al. 2009; Freschet et al. 2010; van Bodegom et al. 2014; Koven et al. 2015a);

• Enhanced understanding of the climatic and edaphic controls on shrub expansion including uncertainty analysis and new benchmarking datasets for spatial and temporal representation of shrubs in large-scale models (Myers-Smith et al. 2011; Jiang et al. 2012; Zhang et al. 2013; Frost et al. 2014; Wullschleger et al. 2014); and

• Improved characterization of hydrology at watershed to regional scales, and integration of surface and subsurface processes that drive water distribution across Arctic ecosystems, especially as a function of disturbance and landscape transitions in a changing climate (Lara et al. 2015; Natali et al. 2015).

ESMs lack many of the key processes that govern interactions between high-latitude ecosystems and climate (Koven et al. 2013a; Hayes et al. 2014; Schaefer et al. 2014). Multiple carbon, water nutrient, and energy feedbacks that occur in response to rising temperatures must be resolved if we are to improve model prediction of climate. Observations suggest that permafrost thaw is now occurring throughout the Arctic (Jorgenson et al. 2006; Romanovsky et al. 2010) and is expected to drive changes in climate forcing through biogeochemical and biophysical feedbacks. Biogeochemical feedbacks are dominated by the potential to release a large amount of currently stored carbon into the atmosphere as CO2 and CH4 (Schuur et al. 2015), whereas biophysical feedbacks include terrestrial radiation, and sensible and latent heat flux budgets that can lead to large-scale warming (Swann et al. 2010) and that are caused by, among other factors, changes in vegetation distributions (Chapin et al. 2005; Euskirchen et al. 2009). These processes will take place not only in response to warmer temperatures, but also within an environment undergoing geomorphic change and landscape reorganization (Rowland et al. 2010; Grosse et al. 2011). Thawing of ice-rich permafrost can lead to subsidence and deformation of land surfaces that can dramatically change topography, surface and groundwater hydrology, biogeochemistry, and vegetation structure on timescales of years to decades.

Although existing representations of land surface processes in ESMs describe some of the many relationships that exist among vegetation, biogeochemistry, and climate, these representations are highly uncertain and require extensive confrontation with observations to test and improve models (Bouskill et al. 2014; Fisher et al. 2014). Furthermore, many of the coupled properties and processes related to permafrost thaw, surface and subsurface interactions, and soil moisture (Vogel et al. 2009; Natali et al. 2015) are not currently explicitly represented in process models. The presence of ground ice and cryostructures, for example, and their influence on surface topography appear to be critical drivers of landscape-scale processes (Belshe et al. 2013) but cannot be resolved at even the highest resolutions


presently conceived for global-scale climate models (Aleina et al. 2013; Lee et al. 2014). This underscores the importance of conducting holistic, multidisciplinary investigations of the Arctic and representation of ecosystems in models as dynamic, evolving, highly coupled systems (Slater and Lawrence 2013).

Accurate prediction of long-term ecosystem climate feedbacks in the Arctic depends in part on the ability of ESMs to represent the critical aspects of land surface climate, such as mean temperatures and precipitation, which control the formation and loss of permafrost, the water and energy balance of watersheds, the function of vegetation, and the burial and potential decomposition of soil organic material. It is encouraging to note that the recent movement toward high-resolution ESMs is providing a more realistic surface climate in the Arctic (Figure 1). Thus, the opportunity exists to match this resolution with an underlying understanding of ecosystem processes and consequences for climate.

As climate predictions improve, we can reasonably set goals for improved models of Arctic ecosystem processes. To assist in setting these goals, there is a need for high-resolution land ecosystem simulation capabilities that allow explicit representation of properties and processes at the spatial and temporal scales where they occur, and methods to incorporate the effects of these fine-scale processes into larger-scale model representations. Such high-resolution modeling can only be achieved through synthesis of new knowledge and understanding of processes emerging from mechanistic studies carried out in the field and in the laboratory. Thus, to cover this broad set of processes and model requirements, we identify five overarching science questions that will guide our research in Phase 2:

Q1. How does the structure and organization of the landscape control the storage and flux of carbon and nutrients in a changing climate?

Q2. What will control rates of CO2 and CH4 fluxes across a range of permafrost conditions? Q3. How will warming and permafrost thaw affect above- and belowground plant functional traits,

and what are the consequences for Arctic ecosystem carbon, water, and nutrient fluxes?

Figure 1. Present-day mean temperature and precipitation for Alaska as predicted by a coupled ESM at standard horizontal resolution (left, ~1°), at high resolution (center, ~1/4°), and compared to very high resolution gridded surface weather based on observations (right, 1 km). Image credits: left—Jiafu Mao; center—Salil Mahajan; right—Michele Thornton.


Q4. What controls the current distribution of Arctic shrubs, and how will shrub distributions and associated climate feedbacks shift with expected warming in the 21st century?

Q5. Where, when, and why will the Arctic become wetter or drier, and what are the implications for climate forcing?

Just as we have done in Barrow, we will use variation in the structure and organization of the Seward Peninsula landscape to guide a series of process-level investigations (Questions 1 through 3) that will be nested at scales ranging from core to plot, landscape, and watershed levels. Knowledge derived in these studies will identify mechanisms controlling carbon, water, nutrient, and energy fluxes, which will then be brought to bear on two integrative and timely questions concerning the future of the Arctic in a changing climate (Questions 4 and 5).

Our multi-scale measurement and modeling approach developed in Phase 1, and now proposed for further use in Phase 2, is motivated by three major deficiencies in current ESMs. These are (1) inadequate high-resolution representation of land surface heterogeneity, including the temporal transition of landscapes (i.e., evolution of the land surface) with projected warming, (2) distribution and fate of permafrost and associated vulnerability of stored carbon to loss back to the atmosphere, and (3) biophysical feedbacks to climate brought about by changes in vegetation dynamics and especially shrub migration along the boreal-tundra transition zone. The following sections highlight our 10-year vision for the NGEE Arctic project where we set forth a series of milestones that will lead to the resolution of the above-mentioned deficiencies. We also elaborate on Phase 1 accomplishments and outline plans for Phase 2 where we not only propose to continue our research on the North Slope, but to expand those efforts to the Seward Peninsula.

VISION P a g e | 7

3. VISION The NGEE Arctic project is envisioned as an integrated effort to dramatically reduce uncertainty in ESM projections, and to increase scientific understanding of how high-latitude ecosystems will respond to climatic and atmospheric change. Increasing our confidence in climate projections for high-latitude regions of the world will require a coordinated set of model-inspired investigations that target improved process understanding and model representation of important ecosystem-climate feedbacks. Our vision and our approach to achieving that vision are ambitious and will require a focused effort from our large and multidisciplinary team. Continued research on the North Slope of Alaska and the addition of new sites on the Seward Peninsula will allow novel questions to be asked and answered in ways that broaden our spatial and temporal perspective of the Arctic. Our ultimate goal is to support the BER mission to advance a robust predictive understanding of Earth’s climate and environmental systems by delivering a process-rich ecosystem model, extending from bedrock to the top of the vegetative canopy/atmospheric interface, in which the evolution of Arctic ecosystems in a changing climate can be modeled at the scale of a high-resolution ESM grid cell. Major milestones identified across the three phases of the NGEE Arctic project allow us to judge progress towards this goal (Figure 2).

The integrated and interdisciplinary perspective forged by our team in Phase 1 will be foundational to Phase 2 activities as we move toward using model sensitivity and uncertainty analysis and new process knowledge to guide modeling, experimental, and observational efforts toward improved climate predictions in high-latitude ecosystems. By expanding our scope in Phase 2 to consider ecosystems underlain by warmer, discontinuous permafrost and spanning a broader range of topographic complexity,

Figure 2. Milestones by project phase.

VISION P a g e | 8

NGEE Arctic provides a significant advance toward our ultimate goal of pan-Arctic process knowledge to improve climate prediction. In Phase 2 we will expand our approach with increased use of airborne and satellite imagery. This will facilitate upscaling our field and landscape-scale observations to regional scales, and encourage interactions with the DOE’s ARM and ASR programs and cross-agency collaborations with NSF (NEON), NOAA, USGS, and NASA through their CARVE and ABoVE campaigns. Furthermore, NGEE Arctic will have an increased emphasis on plant traits and trait-enabled modeling in Phase 2 that positions the project at the cutting edge of understanding and modeling dynamic vegetation. This new emphasis on plant traits provides the opportunity for close collaboration with sister project NGEE Tropics to harness the important variation in plant functional traits across the globe to inform and improve the ACME model. We will continue to collaborate with the TES SFA at Argonne National Laboratory as together we share knowledge and samples that can be used by the SFA to develop regional maps of a soil carbon stocks and their intrinsic decomposability for model benchmarking. We will also continue to make datasets gathered in the field and laboratory publicly available through the NGEE Arctic data portal and contribute key data for model evaluation and benchmarking efforts (e.g., ILAMB).

We will continue to foster a strong interaction with DOE’s Earth System Modeling program. In Phase 2 we expect to benefit from climate-scale developments emerging from the ACME effort while continuing to drive future generations of ESM development through improved ecosystem process representation from bedrock through soils and vegetation canopy to the atmospheric boundary layer. Specifically, our Phase 2 efforts will adopt and extend ACME developments in the areas of subgrid heterogeneity and topographic down-scaling to inform and improve our ability to place new fine-scale Arctic process understanding in a climate-relevant context. The result will be a much more explicit representation of processes operating at multiple scales within future generation ESM grid cells. In Phase 3 (2019 to 2022), we expect to be in a strong position to conduct pan-Arctic simulations using a model with unparalleled sophistication in its cross-scale process representation that is parameterized and evaluated against a multi-scale, nested hierarchy of measurements and synthesis products. Our long-term vision is to establish a process-rich land and ecosystem science capability within future generation ESMs, making effective use of expanded compute power anticipated on the path toward exascale computation.

PROGRESS AND ACCOMPLISHMENTS P a g e | 9

4. PROGRESS AND ACCOMPLISHMENTS Our progress to date includes results from data synthesis, field observations, laboratory experiments, model development and simulations, publications, presentations, and outreach. Our team has published 64 journal articles, including manuscripts in Nature and PNAS, and given presentations in the United States, Russia, Europe, and Asia. NGEE Arctic scientists convened workshops and chaired sessions at major conferences, mentored students, post-doctoral candidates, and junior staff, and participated in educational activities not only at our home institutions but also in Barrow and Nome. These efforts ensure that our research is visible and promotes the science goals of the larger community and the mission of the BER Climate and Environmental Sciences Division (CESD).

In this section we highlight the impact of our Phase 1 research. Significant accomplishments illustrate that we have successfully (1) established the physical infrastructure required to support field activities on the Barrow Environmental Observatory (BEO); (2) undertaken multi-scale characterization of tundra landscapes that guide our modeling and scaling strategy; (3) used this land surface understanding to organize our field and laboratory studies; (4) integrated an iterative ModEx philosophy into our observational, experimental, and modeling activities; and (5) created a public resource that makes data and metadata available in a searchable format to the larger community (see Appendix).

ESTABLISHMENT OF PHYSICAL INFRASTRUCTURE During Phase 1, the NGEE Arctic team deployed several sophisticated measurement capabilities at our field sites near Barrow (Figure 3). These systems were strategically designed to provide (1) key spatial and temporal datasets for use in multi-scale model testing and evaluation, (2) multi-parameter datasets that couple critical properties and processes, (3) continuous and autonomous operation in harsh environments, and (4) automated data capture for sharing and archiving.

Figure 3. NGEE Arctic intensive field sites, transects, and instrument deployments on the Barrow Environmental Observatory.


Permafrost/active layer temperatures and micrometeorological stations: Monitoring stations were located across four intensive study sites: (A) low-centered polygons with well-defined troughs, (B) high-centered polygons, (C) flat-centered or transitional polygons, and (D) inundated low-centered polygons with no troughs. Stations monitor active layer and permafrost temperatures; soil moisture, thermal conductivity, and heat flux; snow depth, air temperature and humidity, wind speed and direction, long and shortwave radiation, and summer precipitation. This information serves as calibration and evaluation data for fine-scale simulations of polygons. Data are available in real-time and as annual summaries.

Geophysical monitoring system: Autonomous electrical resistance tomography (ERT) and soil temperature and soil water content probes were co-located with tower-mounted sensors on the BEO to provide real-time and coincident sensing of above- and belowground processes. ERT and soil probes are installed along two transects to measure belowground processes, whereas tower-mounted sensors record images that help interpret land surface characteristics at the same locations. These data provide information on surface-subsurface interactions which is used in fine- and intermediate-scale modeling. Data are relayed once a day to a central server at Lawrence Berkeley National Laboratory and then made available as quality controlled datasets at the NGEE Arctic data portal.

Eddy covariance tower: An eddy covariance system quantifies fluxes of carbon, latent heat, and sensible heat. Tower instruments include LI-7700 (CH4) and LI-7500A (CO2/H2O) IRGAs, Gill R3-50 Sonic Anemometer, and CNR4 radiometer. This tower is paired with a second system deployed several kilometers to the northeast in connection with the ARM program. Carbon and energy fluxes serve to evaluate process-based models and test up-scaling strategies. A three-year dataset of flux values has been submitted to AmeriFlux and NGEE Arctic data portals where they are available for download.

Energy balance tram: The NGEE Arctic tram consists of 65 m of elevated track located within the eddy covariance tower footprint (Figure 4). It consists of a fully automated cart equipped with a suite of radiation and remote sensing instruments. Data are transmitted every hour and archived annually at the project data portal. Data from the tram are being used in conjunction with surface CO2 and CH4 fluxes

and geophysical and temperature measurement systems to provide a robust understanding of Arctic tundra land surface conditions and processes, and datasets of model evaluation and benchmarking.

Spatial array of water depth sensors for landscape hydrology: Seasonal dynamics in surface hydrology across the four intensive study sites (A, B, C, and D) are measured with water level sensors deployed in 44 shallow wells. These instruments (Schumberger DI701 Cera-Diver) are designed to be suspended in small diameter wells. Data are used to evaluate model simulations of thermal hydrology for polygonal landscapes. Seasonal water level data are available at the NGEE Arctic data portal.

Figure 4. A sensor-laden cart traverses the landscape every 3 h. The tram is co-located within an eddy covariance footprint and geophysics ERT array, with continuous measurements of soil moisture and temperature and periodic estimates of CO2 and CH4 flux using static chambers.


MULTI-SCALE CHARACTERIZATION OF LANDSCAPES ACROSS THE BARROW PENINSULA A central NGEE Arctic hypothesis is that significant uncertainty in ESM predictions of climate impacts and feedbacks in the Arctic is due to poor representation of the fine-scale heterogeneity in landscape properties and processes. Research conducted in Phase 1 incorporated a suite of airborne- and satellite-based measurements to identify unique geomorphological features across the Barrow Peninsula and to spatially extrapolate surface and subsurface measurements. Extensive use of a light and radar (LiDAR) dataset provided to NGEE Arctic by Craig Tweedie allowed for initial topographic characterization of the Barrow landscape (Hubbard et al. 2013). Gangodagamage et al. (2014) developed approaches to characterize tundra geomorphology using a suite of topographic metrics (e.g., directed distance, slope, and curvature) to identify and classify polygonal features such as rims, troughs, and centers. These classification metrics, in combination with NDVI, were used to spatially extrapolate active layer thickness (ALT) across the polygonal landscape and to provide polygon delineations for both high- and medium-resolution modeling domains. The availability of such domains made possible the spatial mapping of plant communities based on microtopography (Sloan et al. in preparation). Field data were used to define key plant communities and to adjust the geomorphological polygon delineation to reflect vegetation boundaries, thus enabling determination of the fractional coverage of plant communities within the wider landscape for use in models (Figure 5). Langford et al. (in preparation) combined multi-spectral remote sensing from the WorldView-2 satellite with LiDAR-derived DEMs to spatially extrapolate community composition data from vegetation surveys using spectral and topographic characteristics. These maps serve several uses including input for plant functional types (PFTs) and to scale key measurements such as ecosystem CO2 and CH4 fluxes.

Multi-spectral imagery was also used to develop a geomorphology map for an 1800-km² study area on the Barrow Peninsula. The map was used to estimate carbon fluxes for the study area and to address how different land cover types disproportionately influence hydrology, plant community composition, and biogeochemistry. Using an intermediate-scale Arctic terrestrial ecosystem model, Lara et al. (in preparation) showed the profound role of microtopography and, in particular, wet geomorphic features

Figure 5. Shaded relief map showing survey transects across a low-centered polygon (left) and map of the distribution of plant communities created from intensively collected ensemble statistics including field vegetation surveys and LiDAR-derived metrics (right).


(troughs, low-center polygons, ponds, and lakes) on plant productivity, soil carbon, and carbon flux from the landscape. Model uncertainty in many of these processes increased with increasing spatial resolution (0.003 to 100 km2). It was shown that representation of tundra landscapes at a resolution of 4 km2 generally minimized model error (Figure 6). While this work is ongoing, it suggests that simulating heterogeneous tundra landscapes at fine scales yields the least uncertainty and sets a hypothesized level of resolution for future model development. These results demonstrate that uncertainty in ESMs can be reduced through improved representation of the sub-grid heterogeneity due to quantifiable geomorphic features that control the spatial distribution of soil moisture, inundation, and ecosystem function.

Use geomorphology to structure and organize our field and laboratory investigations Thermal-hydrology and geomorphology: A goal of the NGEE Arctic project in Phase 1 was to collect and migrate thermal-hydrology data into a scaling framework to initialize, parameterize, and evaluate models. Such a framework would enable the prediction of the climate-driven evolution of the Arctic landscape as permafrost thaws. Through co-located measurements of surface and subsurface properties at our intensive study sites, we quantified the interactions between landscape geomorphology, ground temperature, snow pack, hydrology, and peat thickness. These data show strong temporal variation in the propagation of seasonal freezing and thawing fronts in the active layer both between polygon types and their associated polygon features (i.e., rims, troughs and centers). This spatially distributed thermal response is due to the co-variation of snow depth, peat depth, and soil saturation associated with microtopography (Atchley et al. in review). The relative influence of these topographically distributed properties was quantified by calibrating a process resolving thermal-hydrology model with in situ data and performing ~4000 simulations spanning the range in peat thickness, snow depth, and saturation found at our BEO field site (Figure 7; Atchley et al. in preparation). This provided a thermal-response function that can be applied to any point in the Barrow landscape for which we have polygon feature characterization data (Skurikhin et al. 2013 and 2014; Gangodagamage et al. 2014; Lara et al. 2015; Wainwright et al. 2015). This thermal response function can now be used in reduced order or statistical models to predict the distribution of future thaw depths and the influence of spatially variable future active layer deepening on degradation of permafrost and ground ice.

Figure 6. (a) difference in soil carbon relative to the highest resolution (30 × 30 m) estimates and (b) projected differences in landscape-level accumulated soil carbon in teragrams between 1970 and 2100 associated with differing spatial resolution.


As with subsurface temperature, we found that hydrologic response varied between polygon types but with added complexity due to the topographic connectedness of polygon features surrounding each water level monitoring well. The most straightforward water level behavior occurred in the centers of polygons at Areas B and D. Water levels at Area D (low-centered polygons with rims but no troughs in a partially inundated drained thaw lake basin) remained above, at, or near the ground surface throughout the summer and had a damped response to rainfall and prolonged dry periods. In contrast, the water table response in Area B (high-center polygons) had a “spiky” water level behavior with a rapid rise and fall in response to individual rain or snowmelt events. A much more diverse set of responses were measured in polygon centers at Areas A and C and in troughs across all four polygon types, and these responses are dependent on local topographic connectedness. These data provide insights into the fate of surface water as landscapes potentially change into the future and to the question of whether the Arctic will get wetter or drier with warming (Harp et al. in review). Analyses are underway to explore the consequences of these findings to larger landscapes (Liljedahl et al. in preparation) and to use these insights to describe, and ultimately model, lateral connectivity among polygons across a watershed domain.

At the landscape scale, and in support of the results inferred from surface water levels, NGEE Arctic researchers are linking satellite and kite-based analyses with time-lapse photography, ground surveys, and eddy covariance measurements to characterize and model the seasonal dynamics of inundation in drained thaw lake basins, polygon ponds, and wetlands across the BEO. An early application of a water balance and runoff model to representative polygon domains shows flat- and low-center polygons impound nearly twice the water as high-center polygon domains (Liljedahl et al. 2012). New models developed in Phase 1 (Painter et al. 2013, 2014; Karra et al. 2014; Coon et al., in revision; Pau et al. 2014; Liu et al. 2015) are being applied to fully characterize the coupled thermal-hydrologic response for the full range of geomorphic land units, polygon types, and polygon features that comprise ice-rich polygon landscapes. The simulations aim to predict which land units will become wetter or drier under future climate scenarios, since this will control future ecosystem response and associated climate feedbacks.

Cross-scale comparison of CO2 and CH4 flux and implications for models: Field and laboratory experiments identified significant temporal and spatial variation in greenhouse gas (GHG) production and emission throughout the thaw season on the BEO. Observations of an early CH4 release from soils during thaw, the rapid onset of respiration following snowmelt, and the delayed production of CH4 forecast changes in carbon dynamics due to a warmer and longer thaw season in the Arctic. Together with observed microtopographic variation in GHG production across the polygonal landscape features (Wainwright et al. 2015) and improved understanding of the biogeochemical and hydrological controls on

Figure 7. Three colored contour maps of active layer thickness (ALT) as a function of peat thickness and snow depth (here a snow multiplier ranging from 0.01 to 3.0 corresponds to a snowpack depth ranging from ~ 0.01 m to 1.3 m) are slices through an ensemble domain of ~4000 simulations using the Arctic Terrestrial Simulator calibrated to NGEE data for a Barrow soil column. Each map represents a specific drained water table depth ranging from 2 cm (left) to 51 cm (right) below the surface. The driest condition (51 cm) shows less variation in ALT across the peat thickness vs snow depth parameter value space than the wetter conditions [25 cm (center) and 2 cm]. While ALT is shown to increase with increasing saturation, peat thickness is the strongest control on ALT depth.


soil organic matter (SOM) degradation, these observations are improving process understanding to inform models of the Arctic carbon cycle.

There are currently two broad classes of models to represent subsurface biogeochemistry: those that do not explicitly represent microbial processes and substrate interactions with soil minerals, and those that do. Many of the observed biogeochemical responses in manipulation experiments have recently been described as being strongly dependent on microbial and surface processes, and we have worked in the NGEE Arctic project to develop new model structures in CLM and the reactive transport solvers PFLOTRAN and CLM-BeTR to improve our modeling capability for high-latitude systems. Briefly, we developed, tested, and applied new model structures to represent (1) SOM and enzyme interactions with minerals (Riley et al. 2014, Tang and Riley 2015); (2) trait-based microbial representation of nitrification (Bouskill et al. 2012); (3) trait-based microbial representation for vertically-resolved SOM decomposition (Riley et al. 2014); and (4) thermodynamic constraints on the temperature sensitivity of N cycle dynamics and isotopic fractionation (Maggi and Riley 2015). We are also working to integrate these processes into ACME (Zhu and Riley 2015). An important component of this effort is the concurrent development of benchmarking strategies for model testing. To that end, we focused on developing a meta-analysis of pan-Arctic tundra warming and nutrient addition experiments (Bouskill et al. 2014). This work illuminated critical high-latitude processes that were very poorly represented in climate models but that are required for accurate carbon-climate feedback assessments. Our NGEE Arctic modeling effort is using these results to inform our ongoing model developments.

In addition to Bouskill et al. (2014), a complementary series of field and laboratory compared potential GHG production and emission from low-centered to high-centered polygons and across their center, rim and trough microtopographic features. Soil incubations at field-relevant temperatures and anoxic conditions in the laboratory showed that the highest rates of CO2 and CH4 production occurred in the organic horizons of low-centered polygon soils. The cumulative GHG production was positively correlated with soil water content in the organic horizons of the polygonal center, ridge and trough positions and demonstrated higher variability. However, microtopography was a less important predictor for mineral horizon soils, which showed less variability in CO2 or CH4 production. Methanogenesis rates declined more rapidly at low temperatures than CO2 production, consistent with field-scale observations. Mineral horizons released substantial quantities of CO2 from anaerobic respiration and fermentation that began immediately after soil thawing. Methane was produced following a long lag period during which methanogen population sizes increased by several orders of magnitude (Figure 8). Field measurements of CO2 and CH4 dissolved in soil water also identified increased CH4 concentrations late in the thaw season, concurrent with a decrease in organic acid concentrations and a potential shift in methanogenesis pathways from acetoclastic to hydrogenotrophic methanogenesis. Methane, iron, and many inorganic ion concentrations increased with depth as the active layer thawed annually. Geochemical differences in soil water chemistry were greatest in comparisons of high-centered polygon trough and center samples, reflecting significant differences in drainage and dissolved oxygen (Newman et al. 2015). These fine scale measurements

Figure 8. Cumulative CH4 and CO2 production from anoxic incubations of low-centered polygon mineral soil at 8°C.


provided a mechanistic understanding of the biogeochemical processes that were integrated by larger-scale measurements.

An eddy covariance tower deployed on the BEO quantified fluxes of CO2 and CH4 at an intermediate spatial scale, by surveying an area of undisturbed polygonal tundra. Soil temperature and water content were continuously measured in the intensive study areas. Net ecosystem exchange (NEE) values were positive after snowmelt and initial thaw, reflecting the rapid onset of respiration. Short releases of CH4 also followed snowmelt, with marked “pulses” of CH4 observed during a 2-week period in early spring that coincided with snow and surface-ice melt across our study site. Similar pulses of CH4 were observed on a series of frozen and then thawed permafrost cores subjected to controlled-temperature studies in the laboratory, suggesting a possible physical capping of soils with ice that, when thawed, allowed the released of underlying CH4 (Raz Yaseef et al., in preparation). We are currently collaborating with the NASA-sponsored Carbon in Arctic Reservoirs Vulnerability Experiment (CARVE) project to explore a multi-scale explanation for what is also observed as significant emissions of CH4 in the spring at regional scales on the North Slope of Alaska.

INTEGRATION OF MODEX PHILOSOPHY INTO MODELING AND SCALING STRATEGIES Representation of Arctic plant photosynthesis in Earth System Models: Photosynthetic CO2 uptake is a critical uncertainty in ESM projections due, in part, to poor parameterization brought about by a lack of observational data. A key parameter in this uncertainty is the maximum rate of CO2 carboxylation (Vc,max) by the photosynthetic enzyme Rubisco. Sensitivity analysis, model simulations, and efforts to identify model parameter uncertainty show that model estimates of gross and net primary productivity (GPP) are particularly sensitive to Vc,max, and those parameters used to derive it (Thornton and Zimmerman 2007; Bonan et al. 2011; Friend 2010; LaBauer et al. 2013; Rogers et al. 2014; Sargsyan 2014).

In Phase 1, we sought to reduce the uncertainty associated with model representation of photosynthetic capacity. We examined model representation of Vc,max in the land component of ESMs in the C4MIP and CMIP5 model inter-comparison projects (Friedlingstein et al. 2006, 2014). Only three ESMs explicitly included PFTs for the Arctic (AVIM, BETHY and CLM). We found that the data used to derive Vc,max in these models relied on a small number of datasets and unjustified assumptions (Rogers 2014). In CLM, we found errors in the global constants used to convert leaf N (Na) to Vc,max that when corrected were found to result in a 10 Pg C yr-1 reduction in modeled global GPP (Rogers 2014).

In Barrow, we measured Vc,max for seven Arctic species covering four PFTs (dry and wet tundra graminoides, deciduous shrubs, and forbs). We found that ESMs were markedly underestimating the potential for CO2 assimilation in Arctic PFTs (Figure 9). Concurrent measurement of Na, the fraction of leaf N invested in Rubisco (FLNR), and the turnover rate of Rubisco (kcat) showed that CLM markedly underestimated Na and FLNR resulting in low estimates for Vc,max (Rogers et al. 2015a, in preparation). In addition, the ratio of the maximum electron transport rate (Jmax) to Vc,max - the JVratio is fixed globally for most ESMs, and it determines the CO2 responsiveness of a PFT to rising [CO2] (Rogers et al. 2014; Rogers et al. 2015b, in preparation). We also found that the JVratio is higher than that currently used in many ESMs (Medlyn et al. 2002). This suggests that Arctic photosynthesis will also be more responsive to rising [CO2] than previously thought (Rogers et al. 2015a, in preparation). Early investigation of temperature response functions (TRFs) of Vc,max also suggest that the TRFs used in current ESMs would underestimate the capacity for CO2 uptake at low temperatures (Rogers et al. 2015a in preparation). In short our data have shown that the Arctic tundra has a much greater capacity for CO2 uptake, particularly at low temperatures, and will be more CO2 responsive than is currently represented in ESMs.


If models are to more accurately represent CO2 uptake they will need to account for temporal and spatial variation in key photosynthetic plant traits – such as Vc,max – and link those mechanistically to N partitioning and N acquisition (Thornton and Zimmerman 2007; Iversen et al. 2015; Slette et al. 2015, in preparation). Using information from NGEE Arctic and the TRY database, Ghimire et al. (2015) improved the plant model for CLM4.5 with mechanistic representations of (1) plant N uptake based on root scale Michaelis-Menten kinetics; (2) linkages between leaf N and plant productivity based on observations in the global TRY database and several individual studies, including NGEE Arctic; and (3) plant N allocation. Moreover, data collected by our team are being used in several synthesis activities to address environmental controls on Vc,max (Ali et al. 2015, Ali et al., in preparation) and optimal stomatal behavior (Lin et al. 2015).

Site-calibrated, integrated surface/subsurface thermal-hydrology simulations: A central tenet in the NGEE Arctic scaling strategy is that the reliability of process models will be enhanced by comparing to observations at their natural scales prior to upscaling to climate-relevant scales. However, it is challenging to simulate the thermal-hydrology of soils experiencing freeze/thaw cycles at the level of detail required for comparison to local-scale observation. The simulation challenges are a result of the numerical difficulty in representing freezing phenomena in soils and the need to simultaneously represent many coupled processes on the surface and subsurface (Painter et al. 2013). A key accomplishment in Phase 1 was a simulation capability that couples models for thermal and overland flow processes on the surface, snow distribution in sub-polygon-scale microtopography, and 3D subsurface thermal hydrology (Figure 10). The development of an innovative system for managing complexity in multi-physics simulations (Coon et al. 2015), advances in surface/subsurface model coupling algorithms, and advances in surface and subsurface process models including the mathematical representation of moisture movement in freezing/thawing soils (Painter and Karra 2014; Karra et al. 2014) were critical to achieving those first-of-a-kind simulations.

This capability developed in Phase 1 enabled an iterative ModEx approach that goes beyond traditional parameter estimation to address model structural refinements (Atchley et al. 2015). Specifically, we used borehole temperature data as calibration targets in an inverse model to estimate thermal soil properties. We monitored both the model goodness of fit and calibrated parameter values relative to ranges measured directly in previous studies as metrics. When either of those metrics was judged to be too large, the model representation itself was adjusted. That successive refinement of the model structure resulted in improved fits to data on active layer depth and profiles of permafrost temperature gathered by field research teams in 2012-2013. In addition, uncertainty in the estimated parameters was quantified using a calibration-constrained approach (Harp et al. 2015) based on the null-space Monte Carlo method. This activity

Figure 9. Maximum carboxylation capacity of Rubisco (Vc,max) measured in seven tundra plant species (open bars) compared with current parameterizations used in selected ESMs (filled bars).


demonstrated that borehole temperature measurements can be effectively used to reduce uncertainty in soil thermal parameters.

The calibrated model is being used in a series of numerical experiments that explore sensitivities of projected permafrost thaw and carbon release to model assumptions and parameter values, uncertainties in projected conditions, and potential model simplifications for use at larger scales. Using CESM climate projections in the RCP 8.5 scenario, we project the active layer thickness to be between 0.8 and 1.2 m by the end of the century, where the uncertainty is a consequence of uncertainty in soil thermal parameters. Preliminary results of 2D simulations suggest that spatial variability in thermal conditions at and below the scale of a single polygon is mostly limited to systematic variation in soil organic content (thickness of a peat layer), and not fine-scale soil moisture variation. Those results suggest a computationally efficient approach to representing the thermal hydrology of lowland tundra at large scales based on 1D soil columns that are mutually independent but coupled indirectly through surface water flows. The IDEAS project has adopted this hybrid 1D/2D approach to intermediate-scale simulations as a use case and is providing guidance and tools to facilitate the necessary software refactoring.

Identification of function co-variation across tundra landscapes: A significant challenge associated with modeling ecosystem feedbacks to climate is the quantification of surface and subsurface interactions among permafrost, hydrology, vegetation, geochemistry, and microbiology. Although past studies identified correlations among ecosystem properties and landscape controls on such properties, there is still uncertainty in identifying the scale at which such interactions (or co-variation) emerge in complex landscapes and how such interactions are related to ecosystem-climate feedbacks.

In Phase 1, NGEE Arctic investigators led a comprehensive above- and belowground characterization of the Arctic tundra to explore the spatial distribution of relevant ecosystem properties in the Barrow landscape and their relationship to geomorphology based on multi-type, multi-scale datasets including in situ measurements, core analysis, geophysics, and remote sensing (Figure 11). Hubbard et al. (2013) and Wainwright et al. (2015) documented the strong co-variability of above- and belowground properties at the NGEE Arctic site and also identified ecosystem functional zones to describe such co-variation using

Figure 10. Simulation capability integrating three-phase subsurface thermal hydrology, two-phase surface flows, topography controlled snow distribution, and surface energy balances was developed in Phase 1 (left panel). Borehole temperature data were used in an iterative ModEx process to refine the structure of that model and estimate model parameters, with dramatic improvement in the model’s ability to reproduce observations (right, top panel). The calibrated model is being used in projections of permafrost dynamics in a warming climate (right, bottom). Preliminary results suggest that active layer depth will increase to more than 1 m by the end of the century and that taliks (persistent unfrozen regions) may develop underneath troughs.


spatially extensive geophysical and remote sensing datasets. Statistical analyses of various datasets showed that such functional zones have a larger explanatory power than fine-scale microtopographic classes for describing the co-variations in key properties, including carbon fluxes and biogeochemistry. The significance of such functional zones was further validated by geochemical analyses. Newman et al. (2015), for example, reported that surface-water and pore-water chemistry showed significant differences among the functional zones, especially for redox sensitive species and nutrients such as dissolved oxygen, nitrate, phosphate, and sulfate. These studies revealed a significant variation in microbial community structure among the zones with distinct metabolic potentials for CH4 production and oxidation (Tas et al., in preparation). In addition, functional zones exerted a significant control on methane production and net surface methane efflux (Smith et al., in preparation).

These studies reveal multiple lines of evidence suggesting co-variability of above- and belowground properties, their influence on the Arctic tundra functioning, and the value of a functional-zone approach for characterizing a range of processes and properties in sufficient detail and over scales useful for climate modeling. Such approaches could potentially be used as emergent and scalable metrics. We intend to use this functional zonation approach as an organizing framework in Phase 2 to explore the co-variability of key ecosystem properties in topographically variable Seward watersheds, to improve our mechanistic understanding of above- and belowground interactions, and to map key properties over large spatial extents for parameterizing ESMs.

Modular coupling of process-resolving permafrost model in a climate-scale land model: We coupled a sophisticated numerical representation of subsurface biogeochemistry and thermal hydrology with the existing vegetation component of an ESM, providing a self-consistent multi-scale system designed to improve knowledge migration from field and laboratory studies into large-scale simulations for improved climate prediction. A goal for this effort was to replace the existing ESM subsurface module with a more capable version having a level of process representation commensurate with the states and processes observable in the NGEE Arctic field and laboratory settings. Our Phase 1 climate-scale model development targeted the biogeochemistry-enabled version of CESM (Lindsay et al. 2014) and its land component, CLM (Oleson et al. 2013). We selected a process-resolving model (PFLOTRAN, Gardner et al. 2015) which is designed to operate at fine spatial scales including 3D (lateral and vertical) transport of heat, water, and multiple interacting (reacting) biogeochemical species, and we modified it to include a detailed representation of freeze-thaw processes and coupling of surface and subsurface water flows.

This CLM-PFLOTRAN coupling was implemented through a modularized interface allowing the coupled system to be configured with a variety of interchangeable components or functional units (Wang et al. 2014a). Biogeochemistry and thermal-hydrology components of PFLOTRAN were treated as

Figure 11. (a) Plan view of kite-based landscape imaging and (b) electrical resistivity inversion cross-sections along the Site 0 transect.


separate sub-modules allowing configurations with one or both of these fine-scale sub-models active. All of the biogeochemistry dynamics native to CLM were migrated into the PFLOTRAN framework as a baseline configuration. New mechanistic representations of CH4 dynamics, nitrification/denitrification, and microbial functional groups were introduced. This new module now enables simulations using a broader suite of geochemical reactions (e.g., aqueous complexation, dissolution/precipitation, ion exchange, and redox) and associated thermodynamic databases.

An important consequence of this coupling approach is that we are now able to use the same modeling framework to perform simulations at fine-scale, with 3D coupling and explicit representation of microtopography, and at climate-scale, with 1D (vertical) coupling and subgrid representation of landscape heterogeneity. This provides some assurance of process consistency as we analyze observations to parameterize and evaluate fine-scale models and then seek new parameterizations at larger spatial scales. We applied the coupled model within the BEO study area at point, plot, and watershed scales, and we used observational datasets from the NGEE Arctic field sites to initialize, parameterize, calibrate and verify the model for multi-decadal simulations.

Spatial scaling and reduced order models: As a complement to explicit process-based coupling of functional modules, we are also exploring the use of reduced order models (ROMs) to estimate the variance structure of explicit fine-scale PFLOTRAN simulations. Using several variants of the Proper Orthogonal Decomposition (POD) approach, we showed that statistical properties of a PFLOTRAN hydrology simulation at 25-cm horizontal resolution in the complex polygonal tundra at the BEO site can be very accurately captured using ROM simulations that are 32 times coarser (Figure 12) (Pau et al. 2014; Liu et al. 2015). We are extending this investigation by constructing and evaluating multiple ROMs for different state variables and fluxes (e.g., moisture, latent and sensible heat fluxes, net primary production).

We also applied several approaches to constrain estimates of the pan-Arctic permafrost carbon-climate feedback. First, using CLM4.5, we showed for an unmitigated warming scenario that the future carbon balance of the permafrost region is highly sensitive to the decomposability of deeper carbon, with the net balance ranging from 21 Pg C to 164 Pg C losses by 2300 (Koven et al. 2015a). Increased soil N mineralization reduces nutrient limitations, but the impact of deep N on the carbon budget is small due to enhanced N availability from warming surface soils and seasonal asynchrony between deeper N availability and plant N demands. This was confirmed using a 15N tracer study on the BEO (Iversen et al., in preparation). Second, we applied a data-constrained approach (including laboratory incubations) to estimate the permafrost carbon-climate feedback strength and dynamics (Koven et al. 2015a). Finally, we participated in a review of the interactions between climate, climate change, and the permafrost carbon feedback (Schuur et al. 2015) that highlights many of the mechanisms under investigation in NGEE Arctic.

Figure 12. Predicted 0-5-cm soil moisture at Site A of the NGEE Arctic BEO field study. From left to right: Coarse-resolution PFLOTRAN solution, fine-resolution PFLOTRAN solution, fine-resolution ROM solution, and error in the ROM solution. Errors are typically well below 0.1%.


DATA AVAILABILITY FOR PROJECT PARTICIPANTS AND THE LARGER COMMUNITY Researchers are generating a diversity of data from observations, experiments, and models across field, plot, watershed, regional, and global scales. In support of these many activities, existing tools and expertise have been leveraged to provide data management capabilities to the NGEE Arctic project during Phase 1, and the Data Team have adopted a standards-based, open-source approach to ensure interoperability with future NGEE Arctic systems and other projects (e.g., ARM, NGEE Tropics, and ACME). A project web site (http://ngee-arctic.ornl.gov) was created where participants and the public can access various aspects of the project. The portal provides core project information including goals and objectives, mission, list of participants, contact information, event announcements, recent science activities, and important safety information.

During Phase 1, the data management team assembled the necessary components for the long-term success of the NGEE Arctic data enterprise including knowledgeable staff, developing fundamental data guidance documents, building the computing infrastructure and deploying tools and applications covering the entire data lifecycle, and networking and outreach. Data-related policies have been created, along with data submission guidelines, and data citation recommendations. Established workflows were developed for metadata review, data submission, and the generation of NGEE Arctic-specific Digital Object Identifiers (DOIs). A data submission/file management tool was deployed for participants wanting to submit data to the archive and to share with other NGEE Arctic investigators or the general public. An Online Metadata Editor (OME) was customized and deployed. OME provides a web-based form that investigators can use to describe their individual projects and data. A cataloging tool – Mercury – was adapted to harvest NGEE Arctic documentation files created in OME, check and catalog these entries, and generate a searchable catalog using Solr open-source indexing capabilities. A user interface has been developed to allow users to search this catalog, as well as Arctic holdings collected by other projects (e.g., ARM and CDIAC). During Phase 1, the data portal averaged 500+ visits per week, and approximately 100 of these weekly visits were NGEE Arctic team participants. There are 160+ registered users for the data portal. Capabilities are now in place to track data usage and users, and NGEE Arctic investigators are e-mailed monthly a list of users who have downloaded their data via the project portal.

Three data visualization and manipulation tools were deployed during Phase 1. Automated scripts were developed to harvest in situ weather station measurements made and posted by UAF. These time series can be interactively queried through a visualization tool. A second mapping tool was created to permit users to query and view subset geospatial data including LiDAR data and orthographic photos. The third tool was the development of a site photo viewer to create a “kmz” file for use in Google Earth for viewing images and location descriptions to assist in the Seward Peninsula site selection and characterization process.

IMPACT AND RELEVANCE OF PHASE 1 RESEARCH Research that began in 2012 has moved us toward our goal of delivering a process-rich ecosystem model extending from bedrock to the top of the vegetative canopy/atmospheric interface in which the evolution of Arctic ecosystems in a changing climate can be modeled at the scale of a high-resolution ESM grid cell. We successfully executed a suite of measurements across intensive field plots, transects, and satellite sites near Barrow. These activities tested and applied a multi-scale measurement and modeling framework in coastal tundra on the North Slope of Alaska within which we focused on subgrid heterogeneity in hydrology, biogeochemistry, and terrestrial ecology structured by topography, landscape, and drainage networks. Conclusions drawn from these model-inspired investigations reinforce our hypothesis that representing subgrid heterogeneity is an important consideration if we are to capture the processes at their native scale and represent them in global climate models. These efforts provide guidance for our planned Phase 2 research and datasets, derived products, and knowledge that honor the ModEx philosophy for model initialization, parameterization, process representation, and evaluation.

http://ngee-arctic.ornl.gov/

RESEARCH PLAN P a g e | 21

5. RESEARCH PLAN 5.A MODELING AND SCALING STRATEGY The modeling approach employed in Phase 1 was driven by the recognition that current ESMs fail to capture many of the processes known to control distributions of carbon, water, nutrients, and vegetation communities in Arctic permafrost regions. A founding assumption for NGEE Arctic is that we can improve the predictive skill of climate-scale models by synthesizing existing knowledge at the spatial scales native to the fundamental driving processes and by supplementing that synthesis through new observation and experimentation to fill critical knowledge gaps. In this strategy, models serve as integration tools and are constructed in a way that allows explicit process representation at spatial scales amenable to testing in the field and laboratory. In the course of our investigations, we may discover new processes or new system connections which challenge current assumptions. Our multi-scale modeling system allows objective evaluation of new knowledge as it is uncovered, providing a quantitative and self-consistent framework for the formulation of new working hypotheses (Figure 13).

A guiding principle for our project has been to foster a two-way interaction between process-level understanding and model development at multiple spatial scales up to and including the exchange of parameterizations and knowledge with climate-scale land components of ESMs. As an important outcome of our Phase 1 efforts, we have already demonstrated a successful pathway between new field-informed fine-scale modeling and global-scale process representation. The NGEE Arctic effort led to the coupling of a CLM with a highly resolved subsurface simulator for thermal hydrology and biogeochemistry (PFLOTRAN). This coupled CLM-PFLOTRAN code forms the basis for improved subsurface process

Figure 13. A scale-aware representation of model-observation-experimentation integration (ModEx), recognizing the central goal of new knowledge generation and the use of models and observations at multiple scales to drive the process of new knowledge discovery through uncertainty quantification.


integration into the ACME Land Model, or ALM. In Phase 2, we expect to benefit from climate-scale developments emerging from the ACME effort while continuing to drive future generations of ESM development through improved ecosystem process representation from bedrock through soils and vegetation canopy to the atmospheric boundary layer. Specifically, our Phase 2 efforts will adopt and extend ACME developments in the areas of subgrid heterogeneity and topographic down-scaling to inform and improve our ability to place new fine-scale Arctic process understanding in a climate-relevant context. The result will be a much more process-explicit representation operating at multiple subgrid scales within future generation ESM watershed-based grid cells. This coordination between NGEE Arctic and ACME is facilitated by overlap in the project teams and leadership and is recognized as a mutually beneficial approach to improved science delivery by the top-level leadership for both projects (see letter of collaboration from ACME PI David Bader, Section 10). Strong coordination with other BER modeling, observation, and experimentation is a priority for NGEE Arctic.

Based on assessment of uncertainties in current-generation ESMs and on our team’s experience during Phase 1, we have identified the following five critical effort areas for new scaling and modeling research for NGEE Arctic Phase 2: (1) landscape heterogeneity, (2) biogeochemistry, (3) plant trait variation and distribution, (4) shrub biogeography, and (5) watershed hydrology. We have also identified fine-scale permafrost modeling as an overarching modeling priority which provides essential context for each of the other five science areas. Modeling priorities for improved climate prediction have guided the identification of these areas and have informed the more specific science questions to be tackled in Phase 2. Rationale for the specific science questions is presented together with detailed tasking for field, laboratory, and modeling efforts in Section 5.C (“Integrated Research Plans”). In the remainder of this section we describe the scaling and modeling approach and infrastructure that underpin effort across all of the science questions. We include descriptions of some integrated modeling (IM) and scaling tasks that are necessary elements of our Phase 2 approach but which do not fit neatly into the plans for any single science question.

Arctic Landscape Heterogeneity Heterogeneity of land surface structure and function presents a major scientific challenge for Earth system modeling. For the sake of computational efficiency, and also in the face of sparse observational data, ESMs need to simplify landscape heterogeneity while retaining the components of fine-scale variance that have the strongest influence on aggregated fluxes and states. One approach has been to define relatively large grid cells that represent subgrid heterogeneity by carrying information about the number, type, and fractional area of different land classes within each grid cell. The large grid cells form the basis for communication of fluxes and state information between land and atmosphere, while the subgrid fractions capture processes known to vary among different land classes. Current ESM land subgrid schemes differ in the definition of classes; in the topology of the subgrid (e.g., nested hierarchy or single vector); and in the degree of communication among subgrid elements.

A deficiency in the current generation of subgrid schemes is that although they capture the subgrid area of various land classes they ignore the geography of those classes – how the subgrid regions relate to each other in space and how they relate to regional landforms such as ridges, valleys, and drainage basins (Qian et al. 2010). These considerations are particularly important in complex terrain where near-surface weather and surface and subsurface hydrology are all strongly influenced by elevation, slope, aspect, and surface geology (see Section 5.C, Q1). Vegetation and biogeochemistry are similarly responsive to these landscape variations. Next-generation ESM development in the ACME project is exploring the use of automatically delineated watersheds to replace rectangular grid cells as the top-level organization in a nested spatial hierarchy. The characteristic scale of delineated watersheds is a selectable parameter allowing grids of varying average resolution.

The ACME approach treats subgrid heterogeneity as sub-watershed topographic units organized around variance in elevation, slope, and aspect (Tesfa et al. 2014). Watersheds and sub-watershed topographic


units are delineated from high-resolution (30 m) digital terrain data available globally. A nested hierarchy is used, with topographic units themselves composed of multiple land types representing broad classes such as bare rock, glacier, lake, and unmanaged and managed vegetation. Each land type can itself be composed of multiple “columns,” representing states of mass and energy in a vertically oriented plant-soil subunit including snowpack and plant litter layers resting on the soil surface. Multiple soil columns are also used to capture the carbon cycle and climate consequences of managed and unmanaged disturbances (Wang et al. 2014b). A final level in the subgrid hierarchy describes variation in vegetation types with multiple plant types potentially sharing space on a single column. In this framework all levels below the watershed are spatially implicit meaning that areas are retained but landscape locations are not.

NGEE Arctic Phase 2 modeling will adopt the ACME multi-level subgrid hierarchy as a climate-scale component of our scaling framework and will achieve intermediate-scale process representation by increasing the number of subunits at each hierarchy level and by also retaining detail on the explicit location of subunits within the landscape, as well as lateral connections among subunits (e.g., Cresto Aleina et al. 2013; Shi et al. 2015).

Task IM1: Define climate and intermediate-scale modeling domains. We will use high resolution (5 m) digital terrain data to delineate watersheds and sub-watershed units for our study regions near Barrow and on the Seward Peninsula.

For our Phase 1 model scaling strategy, we included an intermediate-scale modeling effort that was connected to fine-scale and climate-scale modeling through parameter estimation and upscaling; this was conceived as an independent software system. With the new subgrid developments in ACME and our Phase 1 success in linking ALM-PFLOTRAN, we have the opportunity to use the same software system for both climate-scale and intermediate-scale simulation thereby improving consistency in knowledge migration across scales while reducing the complexity of the software system. Parameter estimation across scales remains a critical science focus for the project, and we will be able to devote more effort to that area by using the flexible subgrid scheme of ALM. Our fine-scale modeling efforts will continue, as in Phase 1 to be based on a 3D reactive transport framework with convergence toward a single fine-scale modeling framework for Phase 2 (see more details in this section under Fine-Scale Permafrost Modeling). As we look toward NGEE Arctic Phase 3 deliverables, we also see fine-scale representation of landscape heterogeneity strengthening the relevance of a multi-scale modeling framework for Arctic policy and decision-making (Kraucunas et al. 2015).

Arctic Soil Biogeochemistry We will continue to evaluate the scaling hypothesis put forward under Phase 1 that soil biogeochemical processes are controlled by temperature, moisture, and mineralogy, with organic inputs from new plant production and decomposition of old organic matter in thawing permafrost and outputs as gaseous, dissolved, and particulate flows. This hypothesis implies that the biogeochemistry scaling approach requires spatial scaling of physical model components (thermal and hydrological fluxes and states) and plant dynamics (e.g., fine-root turnover, exudation, competition for nutrients). Special areas of focus in Phase 2 will include new vegetation-biogeochemistry feedbacks associated with position along hillslope flowpaths and up-slope inputs of nutrients, for example through N-fixing shrub communities, as well as modeling temperature responses of microbial processes and their sensitivity to mineralogy and redox state. The ALM-PFLOTRAN framework coupling thermal-hydrology and subsurface biogeochemistry is being incorporated by ACME as a global modeling capability. We propose to implement that capability in Phase 2 and to continue development of new process representations in that framework.

Task IM2: Initialize simulations with coupled thermal-hydrology and biogeochemistry. We will parameterize coupled physics-vegetation-biogeochemistry capability at watershed scales for intensive study sites (see Task IM1), providing initial framework upon which new Phase 2 biogeochemistry studies can build.


A major emphasis for NGEE Arctic research is on improved prediction of net carbon exchange in Arctic ecosystems with special attention to the partitioning among sources and sinks of CO2 and CH4 under conditions of thawing permafrost (see Q2). This is an area where our models depend very strongly on structural evaluation and parameterizations obtained through field and laboratory study. The NGEE Arctic models require information about the chemical composition of fresh plant litter inputs and multiple classes of soil organic matter (e.g., in the active layer and in permafrost), and they also require empirical rate constants describing the dynamics of various SOM decomposition pathways under a range of soil temperature, moisture, and nutrient availability conditions. A new research need emerging from our soil biogeochemistry modeling focuses on the question of time scales for temperature, moisture, and nutrient variation and their influence on microbial dynamics and decomposition. Current model parameterizations for base rates of decomposition and variation in rates associated with changing temperature and moisture are derived from incubation experiments in which temperature and moisture conditions are held constant for extended periods. In our models those same parameterizations are applied to variation on diurnal, synoptic, seasonal, and inter-annual time scales. This assumption of linear scaling in time stands as a testable hypothesis, and coordinated field and laboratory efforts are proposed under Q2 to address it.

Arctic Plant Traits Earth system models represent the structure and function of vegetation, providing a critical biological link in the global energy budget and in the global cycles of water, carbon, and nutrients. Simplifying assumptions are required to accommodate the need for global simulation and in recognition of the uneven availability of parameterization data for plant physiological processes across the global range of vegetation types. For example, the most sophisticated ESMs currently include subgrid fractions of individual grid cells occupied by multiple plant functional types, but those types are parameterized with mostly static (one value per plant type, no time variation) representations of the numerous plant traits that influence energy, water, carbon, and nutrient fluxes. Recent efforts to quantify variation in multiple traits and co-variation among traits have led to modeling improvements at regional and global scales. For example, NGEE Arctic efforts under Phase 1 greatly improved the representation of key plant traits regulating photosynthesis for multiple Arctic vegetation types (Rogers 2014; Rogers et al., in preparation). In addition, new approaches for collecting plant traits using remotely sensed data offer the potential to dramatically increase the amount of plant trait information available for modeling activities (Serbin et al. 2012, 2014, 2015; Singh et al. 2015). Global-scale plant trait databases are also providing useful constraints for modeled plant traits (Kattge et al. 2011), but Arctic plant traits and belowground traits in general are under represented due to sparse data (e.g., Iversen et al. 2015). Early successes have highlighted the value of increased emphasis on realistic plant trait representation in ESMs including the need for plant trait prediction and introducing dynamic trait distributions in place of single, static values.

Task IM3: Initialize simulations with Arctic plant traits. We will bring the best current representations of Arctic plant traits from global databases and NGEE Arctic Phase 1 efforts into baseline simulations and intensive study sites (from Task IM1), providing an initial framework upon which new Phase 2 trait studies can build.

Our Phase 2 efforts will expand on previous work, bringing a major new focus on Arctic shrubs, which were not well represented at the Barrow field site but which play a crucial role in land-atmosphere interactions for energy, water, and greenhouse gases across the Arctic. For maximum benefit to global models, our emphasis will be on building up the sparsely populated Arctic plant trait categories in existing databases through new observations, focused experimentation, and frequent integration of new knowledge in models at fine, intermediate, and climate scales (see Q3 and Q4). A crucial aspect of the scaling strategy for vegetation modeling is to relate plant-scale or organ-scale measurements to plot-scale or canopy-scale properties. For example, we have an established canopy scaling approach that relates distribution of leaf mass, leaf area, nutrient concentrations, and photosynthetic rates to vertical canopy structure (Thornton and Zimmermann 2007). That approach relies on leaf-scale measurements of specific leaf area, leaf nutrient concentration, and photosynthetic potential at the top of the canopy (Rogers 2014)


and on canopy-scale measurements of increase in specific leaf area with canopy depth. The modeling, observational, and experimental teams will work together to extend these scaling approaches to other physiological traits including a focus on the belowground system, which has received relatively little attention. We will coordinate our investigations into Arctic plant traits and trait-based modeling with related efforts under the NGEE Tropics project through participation in community workshops, frequent informal exchange of research findings, and through integration of Arctic-specific findings into the global trait-based modeling framework being developed under the ACME project.

Arctic Shrub Dynamics Due to constraints from physical climate, Arctic tundra has relatively low biomass density and patchy plant distribution. Canopies with low leaf area, low vegetation stature, short growing seasons with rapid phonological transitions, and strong vegetation-snow interactions together produce a landscape energy balance that is uniquely sensitive to shifts in vegetation community composition. Major modes of variation in present day climate and predictions for future climate change depend strongly on the Arctic surface energy balance (Bekryaev et al. 2010; Winton 2006). Vegetation distributions and surface energy balance also drive significant interactions with surface and subsurface thermal hydrology and permafrost dynamics. With inputs to soil organic matter coming from above- and belowground plant litter, biogeochemical processes also interact strongly with vegetation distributions in this heterogeneous landscape. Shrubs play an especially important role in the landscape because they introduce variation in albedo, woody allocation, canopy height, nutrient dynamics, snow interactions, rooting distribution, and hillslope organization (see Q4). ESMs need to be able to predict the current and future distributions of Arctic shrubs and other vegetation types, but in addition to poorly constrained physiological trait parameterization (see Q3), current global models include only very simple approaches to prediction of dynamic vegetation distribution, and even those models have not been carefully evaluated in Arctic tundra landscapes.

Research is under way in the ACME project to introduce a global-scale ecosystem demography (ED) capability in ALM, and this is being integrated with trait-based modeling in NGEE Tropics. Facilitated by overlap in modeling teams, we will collaborate with these developers to bring new dynamic vegetation capability into our Phase 2 modeling framework.

Task IM4: Initialize simulations with Arctic vegetation dynamics. We will parameterize ED capability at watershed scales for intensive study sites (from Task IM1), providing an initial framework upon which new Phase 2 vegetation dynamics studies can build.

We will develop new predictive models representing both the current distribution of vegetation types and the expected shifts in distribution under a changing climate, taking into account interactions with disturbance (e.g., fire, extreme temperatures, wind); changing permafrost; topographic setting; snowpack; hillslope hydrology; and nutrient dynamics. In Phase 2, models will be developed in parallel with data synthesis and new data collection. A common modeling framework will ensure tight integration of these efforts with physiological trait modeling.

Arctic Watershed Hydrology With improved representation of landscape heterogeneity in next-generation ESMs, NGEE Arctic has an opportunity to explore and improve the quality of prediction for subgrid spatial extent of permafrost and its interaction with surface and subsurface hydrology through hillslope flowpaths, vegetation and soil organic matter distribution, and variation in soil depth and mineralogy. Several of the science questions posed for our Phase 2 research plan engage aspects of watershed or hillslope hydrology with a particular emphasis on these dynamics in Q5. To ensure integration across the science question investigations, we will instantiate a full range of multi-scale modeling in each of the intensive study sites using watersheds, hillslopes, and sub-hillslope partitions as the distinct geomorphological units providing spatial organization to our scaling strategy. This approach will provide a common baseline of simulations at the


intensive sites, at watershed, sub-watershed, and fine scales, available for use by our entire team over the Seward Peninsula study sites early in Phase 2. A similar strategy is already being deployed at our Barrow study sites, and that effort will continue in Phase 2.

This common modeling framework approach serves several purposes. First, it prescribes a rapid assessment at each site of data availability compared to a minimum set of modeling requirements. Missing data required for model initialization will be quickly identified and new data collection can be prioritized. Second, it provides measurement and experimental teams with a consistent set of model outputs that can be used to guide sampling strategy and experimental design, in an iterative hypothesis testing framework. Third, it delivers a baseline simulation capability at multiple spatial scales that can be customized for the modeling tasks associated with each science question and progressively refined as new knowledge is gained through those investigations. Fourth, it allows an assessment of the prediction uncertainty and computational cost for model deployment at various spatial scales over extended regions, be that over the satellite sites or over larger regional landscape extents.

Task IM5: Baseline watershed simulations. We will identify watersheds enclosing the intensive study sites from Task IM1 and will implement climate-scale, intermediate-scale, and fine-scale simulations at each site based on available site characterization data and preliminary, prioritized new measurements.

Climate-scale simulations will be based on watersheds as grid cells, with implicit geographic information for subgrid units (area fractions, but not locations within watersheds). Intermediate-scale simulations will be based on the same watersheds, but with explicit geographic information (both area and location), potentially also resolving a larger number of subgrid elements at each level in the nested subgrid hierarchy. Fine-scale models will be implemented as highly resolved (1 to 5 m horizontal resolution) 2D and 3D grids oriented along hillslope flowpaths.

Fine-Scale Permafrost Modeling At the outset of Phase 1, we defined a model-based scaling strategy that used field and laboratory data to constrain fine-scale models of thermal, hydrological, biophysical, and biogeochemical dynamics in polygonal tundra and then we used these fine-scale modeling results to parameterize larger-scale models of the same processes, targeting new process representations and reduced uncertainties in models at the climate-prediction scale. The coupled fine-scale modeling had never been attempted, and to mitigate risk of failure we proposed and executed two efforts based on independent numerical and software engineering frameworks. We succeeded in producing first-ever high-resolution simulations of coupled thermal-hydrology surface-subsurface dynamics for the polygonal tundra environment. We also demonstrated the robust coupling of vegetation and biogeochemistry to thermal-hydrology simulations.

Although significant progress was made in Phase 1, a consensus conclusion emerging from our fine-scale modeling efforts is that existing numerical/software frameworks, as currently configured, are not able to generate fine-scale predictions of freeze-thaw dynamics at the spatial scales required to parameterize larger-scale models of Arctic tundra. After significant development we are able to make one-dimensional (vertical) simulations of freeze-thaw dynamics over multiple years with reasonably fast computation times. Two-dimensional simulations (horizontal transects with vertical resolution) have been demonstrated, but have proven to be more challenging, with simulations occasionally being forced to very slow computation. Three-dimensional problems in realistic terrain, with coupled surface and subsurface hydrology have so far been possible only for small domains (a few tens of meters in the horizontal) and for short time periods (one to several years).

Small simulation time steps have consistently been an issue, preventing us from carrying out fine-scale simulations over large domains, and hindering our progress on the up-scale migration of new knowledge to inform and improve climate-scale prediction. Small time step size is a direct result of the numerical representation of ice-water phase change, which causes mass and energy conservation equations to become extremely nonlinear in the vicinity of freeze/thaw fronts. In particular, a small change in


temperature can create a large change in the energy residual at these critical junctures. As a result, it is difficult to numerically find pressures and temperatures that satisfy the mass and energy conservation equation in each grid cell. Typically, the nonlinear solver fails to converge for time steps of reasonable size, thus requiring the time step to be reduced until a numerical solution can be found. We see similar behavior in both the Arctic Terrestrial Simulator (ATS) and PFLOTRAN, which use different nonlinear solution algorithms.

Based on our experience in Phase 1, we understand the cause of this poor numerical performance and have identified multiple options to improve the time step and move to simulation over larger domains. The options include: modifying the nonlinear solution algorithms; developing more robust time stepping schemes based on solving part of the equation explicitly (implicit/explicit schemes); and making thoughtful approximations to the underlying physics to make the nonlinear system easier to solve.

Task IM6: Fine-scale permafrost modeling. We will assign a small team of specialists, drawn from the two Phase 1 fine-scale sub-teams, to work together over the first year of Phase 2 to solve this fundamental numerical and computational problem.

The problem is now well enough constrained that this experienced task team will work toward a solution at the pure numeric or functional unit level, outside of either the ATS or the PFLOTRAN software framework. The results of this effort will be applied to a single Phase 2 fine-scale modeling framework which merges the realized benefits of our two parallel Phase 1 fine-scale models. Specifically, we will adopt the multi-process coupling framework of ATS and combine that with the vegetation-biogeochemistry coupling currently implemented in ALM-PFLOTRAN.

Model benchmarking Hypothesis testing, model evaluation, sensitivity analysis and uncertainty quantification play a crucial role in the cycle of knowledge discovery shown in Figure 13. Model outputs and hypotheses are evaluated against quantitative knowledge to establish new process study needs, a step which serves as both a filter on multiple competing hypotheses and also as a “ratchet”, moving understanding of the complex multi-scale system forward according to prediction skill and bias reduction in the face of quantified uncertainties. We will formalize this process in our Phase 2 work through a series of model-benchmarking iterations, starting with simulation results from integrated modeling tasks IM2-IM5, evaluated against observational datasets already available from the International Land Model Benchmarking project (ILAMB, supported by DOE’s BER Regional and Global Climate Modeling Program) and from our Phase 1 research. These early uncertainty quantification efforts will result in baseline metrics which will be revisited periodically as new observational, experimental, synthesis, and model development efforts proceed in Phase 2. Developments that result in broad improvements in metrics over baselines will be given more weight in deciding new process study priorities. Specific model benchmarking tasks are called out in Section 5.C, and we will contribute new Arctic-specific benchmarking datasets and evaluation metrics to the ILAMB effort for use by other modeling teams and projects.

Summary table of prioritized modeling needs We have identified the most critical data needs as a common set of prioritized modeling requirements relevant to most or all of the integrated modeling tasks, and also to more specific modeling tasks described in Section 5.C (Table 1). It is recognized that filling these modeling requirements will be an iterative process, and that more complete and accurate information will become available as our Phase 2 studies proceed. Modeling requirement identifiers (MR1-8) are used for easy reference in later proposal sections.


Table 1. List of high-priority modeling data requirements. ID Modeling Requirement Connections to

Science Questions (Section 5.C)

MR1 Digital elevation data and derived products: slope, aspect, horizon angles, multi-scale watershed delineations

1, 4, 5

MR2 Multi-temporal remote-sensing-based maps of vegetation type (species level, when possible), and vegetation state (e.g., leaf area index, fractional canopy cover, biomass)

1, 3, 4

MR3 Plant physiological parameters (traits) for major species, with variance and uncertainty estimates

3, 4

MR4 Sub-daily surface weather inputs: temperature, precipitation, humidity, incident shortwave and longwave radiation, wind speed

3, 4, 5

MR5 Soil characterization: depth to bedrock, texture and organic matter with depth, hydraulic and thermal conductivity

1, 2, 4, 5

MR6 Water table depth and stream discharge for intensively-studied watersheds

2, 4, 5

MR7 Eddy flux measurements: sensible and latent heat, CO2, CH4 2, 4, 5

MR8 Laboratory soil incubations 2

5.B SITE SELECTION AND SAMPLING STRATEGY A critical component of developing capabilities to simulate terrestrial ecosystem-climate feedbacks in carbon-rich permafrost environments is the selection of study sites and development of sampling strategies. Multiple datasets from field sites combined with remote sensing imagery that puts field-scale information into a regional context are required to quantify properties and processes that underpin the five Phase 2 science questions. Such information is also needed to develop and test scaling schemes and to initialize, parametrize, and validate models. Given our long-term goal (Phase 3) to deliver a predictive understanding of pan-Arctic ecosystems and their potential response to a changing climate, we propose in Phase 2 to extend our field, laboratory, and modeling efforts across a representative landscape and climate gradient in Alaska. This strategy will enable our team to compare and contrast knowledge gained across a strong north-south climatic and vegetation gradient and leverage modeling capabilities for regional and pan-Arctic simulations.

NGEE Arctic Phase 1 research focused on the BEO situated on the coastal plain of the North Slope of Alaska. At the largest scale, the hydrological basins of the North Slope comprise thaw lakes, drained thaw-lake basins, interstitial polygonal regions and drainage features with length scales of hundreds of meters. Nested within these land units are ice-wedge polygons of different types. This area was chosen to represent a cold, lowland, carbon-rich, low vegetation-biomass density and diversity, continuous permafrost site located at the northern extent of an Alaskan landscape and climatic gradient. Lowland landscapes such as the BEO comprise ~30% of the Arctic and sub-Arctic. These Alaska coastal plain, river valley, and delta regions have thin (<1 m) active layer soils and deep, ice-rich permafrost. This geometry leads to saturated and flooded conditions across large portions of the landscape throughout the thaw season (Kane et al. 2008), while long periods without precipitation lead to drought-like conditions.


A driving hypothesis of the NGEE Arctic Phase 1 efforts was that polygonal landforms control hydrological stocks and fluxes in the region and that moisture distribution would in turn greatly influence vegetation and soil microbiology and thus components of the carbon cycle and energy balance. As described in Section 4, research in Phase 1 verified this hypothesis, documenting the strong dependencies that exist among geomorphology and carbon cycle processes (Lara et al. 2015), hydrology and biogeochemistry (Hubbard et al. 2013; Newman et al. 2015; Roy Chowdhury et al. 2015), and developing new approaches to extrapolate linked properties that control carbon fluxes at landscape scales (Wainwright et al. 2015).

In Phase 2, we will build upon the success demonstrated in Phase 1 by establishing an observational gradient that includes sites on the North Slope but also on the Seward Peninsula (Figure 14). In contrast to Barrow, this warmer region occupies a highly dynamic transition between Arctic and boreal ecosystems (Epstein et al. 2004). It is characterized by warm, discontinuous permafrost, well-defined watersheds, extensive shallow bedrock, greater diversity in vegetation composition, and this region is experiencing increased frequent episodes of disturbance (Swanson 2010; Jones et al. 2012; Rocha et al. 2012). Approximately 40% of the Arctic can be classified as hilly, vegetated, soil-mantled landscapes with significant carbon stored in active layer soils and permafrost (Grosse et al. 2011; Schuur et al. 2015). Due to the topographic complexity and heterogeneity in permafrost distribution on the Seward Peninsula, we expect greater variability at significantly larger scales in vegetation, biogeochemistry, and permafrost dynamics than observed on the North Slope. A focus on the Seward Peninsula will allow us to compare and contrast knowledge gained on the North Slope, as well as to continue to refine our scaling and modeling approaches gained in Phase 1 to predict ecosystem-climate feedbacks. As described in Section 5A (Modeling and Scaling Strategy), the proposed Phase 2 watershed-centric effort in the Seward Peninsula is not only critical for the NGEE Arctic goal of representing landscapes with strong lateral flows, but it will also provide a natural point of connection to ACME, which treats subgrid heterogeneity as sub-watershed topographic units that are organized around variance in elevation, slope, and aspect.

Figure 14. Transition zone between boreal forest and Arctic and subarctic tundra in Alaska. http://alaska.usgs.gov/science/interdisciplinary_science/cae/boreal_arctic.php


In addition to the contrasting characteristics of the Seward Peninsula relative to the North Slope and their relevance to other Arctic regions, the logistics in the Seward Peninsula region are tractable. This is an important consideration given the need for teams to access the site and to deploy instrumentation in a manner that is safe, cost-effective, and with minimal disturbance to the fragile ecosystem. Our strategy in Barrow has enabled more than 70 scientists to conduct field research on the BEO each summer. While it is unlikely that we can fully replicate this on the Seward Peninsula, our experience suggests that multiple small teams can be deployed safely and efficiently, with larger teams working in intensive field research campaigns. The NGEE Arctic Leadership Team has already taken three trips to the Seward Peninsula (nine to 12 people at a time) to canvass and discuss a range of sites that could serve as Phase 2 test beds. Candidate sites have been identified in three regions of the Seward Peninsula. These include (1) a lowland wet and warm and relatively thin permafrost region with extensive thermokarst and drained lakes near Council; (2) hillslopes and watersheds near Kougarok where vegetation varies with hillslope position and aspect, some previously affected by fire and other disturbance; and (3) along the bedrock-rich areas along the Teller road where hillslope and watershed position, as well as wind, may influence vegetation patterns. In parallel with synthesis of existing research data and analysis of remote-sensing products for the Seward Peninsula, an additional campaign is scheduled for July/August 2015 to acquire samples and perform reconnaissance geophysical imaging at a number of candidate sites. These datasets will be considered together with the Phase 2 science questions in the final site selection process. A characterization tool has already been developed to aid in the visualization, description, and communication of site details within Phase 2 of the NGEE Arctic project.

In addition, a series of synthesis activities will compile relevant data collected at often used field sites that span the tundra area between Barrow and Council including Atqasuk, Ivotuk, Oumalik, Selawik, and Quartz Creek (e.g., McGuire et al. 2003). We will also undertake synthesis activities to compile and analyze data from other regions of Alaska including the North American Arctic Transect (Walker et al. 2008) and sites at Toolik Lake and along the Dalton Highway. We will complement these synthesis activities with representativeness analyses similar to Hoffman et al. (2013). In this way we will be able identify the spatial extent represented by each of the possible satellite sites and down-select in a rational and informed manner.

In conjunction with our site selection strategy, it will also be important to consider site design and sampling. We propose to deploy two different but coordinated sampling strategies during Phase 2. Analogous to the BEO Intensive Study Site, we will develop one site on the Seward Peninsula that will focus all observational and modeling efforts. Within that site, we will develop key intensive sampling transects, parallel and perpendicular to the axis of a chosen watershed. Three parallel transects traversing the watershed will give us replicated sampling of the catena. Plots located at regular intervals, and some stratified by landscape position and vegetation type, will be overlaid on spatially continuous geophysical and remote sensing observations on the transects. Experience at the BEO suggests that such a nested strategy, which includes intensive above- and belowground sampling along geomorphic transects (e.g., Hubbard et al. 2013), can lead to important insights about ecosystem behavior that are difficult to achieve using only stratified, anova-type designs. We envision co-acquisition of datasets along the intensive transects including thermal-hydrogeological, geophysical, vegetation, energy and biogeochemical data. In addition to the Intensive Study Site on the Seward Peninsula and associated intensive transects, we will develop a limited number of satellite sites and transects elsewhere for subgroups to perform specific process investigations related to Q1 through Q5. These sites will be selected to construct controlled (Jenny 1941) comparisons or gradients of, for example, fire chronosequences, permafrost conditions, vegetation types (e.g., alder) or soil mineralogy.


5.C INTEGRATED RESEARCH PLANS QUESTION 1. HOW DOES THE STRUCTURE AND ORGANIZATION OF THE LANDSCAPE CONTROL

THE STORAGE AND FLUX OF CARBON AND NUTRIENTS IN A CHANGING CLIMATE? A fundamental requirement for modeling terrestrial ecosystems is to segment the landscape into discrete regions and assign properties to these regions. A major advance in the ACME modeling framework is the transition from a grid to watershed-based representation of the land surface (Figure 15a; Li et al. 2013; Tesfa et al. 2014; Voisin et al. 2013). Within watersheds the land may be further divided into eco-geomorphic units based on topography, vegetation, soil moisture and inundation, and known or interpreted subsurface properties. Identifying the eco-geomorphic units that fall within a given watershed is challenging but by itself is insufficient to develop a predictive understanding of how terrestrial ecosystems will respond to external drivers such as climate change. It is critical for a modeling framework to represent the spatial relationship of discrete landscape units to surrounding units and to understand their interactions over time to properly capture the influence of lateral transport processes and the sensitivity or resilience of any portion of the landscape to change.

Figure 15. (a) A topographic-based classification of the Kougarok Region of the Seward Peninsula. The black lines show Hydrologic Unit Code watershed at Level 12. The white lines are subwatershed delineated using a 5 m resolution DEM created using IfSAR data. The watersheds have been classified into units based on combinations of elevation (EL), slope, and aspect characteristics. (b) A conceptual illustration of the types of landscape heterogeneities and disturbances present within subwatersheds on the Seward Peninsula. In this conceptualization, the thickness and extent of permafrost varies with hillslope aspect.

Critical to effective integration of knowledge from observations into models is the ability to place ecosystem characteristics into a process-based context. This knowledge integrates the anticipated response of land surface and ecosystem dynamics to future change based on field observations, model simulations and a process-based understanding of how prior geomorphic processes, climate conditions, and disturbances shaped the landscape. The lateral flux of water and nutrients along topographic gradients leads to the spatial and temporal redistribution of these elements and drives hydro-thermal dynamics across the landscape. Watersheds integrate lateral fluxes, and differences in watershed structure drive changes in the timing and magnitude of fluxes associated with topographic position. In addition to the organization of watersheds, the physical properties of the subsurface mediate the storage and flux of


particulate and dissolved constituents (Figure 15b). The spatial organization of soils, permafrost, ice, and bedrock controls the relative influence of vertical and horizontal transport pathways and residence times of carbon, water, and sediments within watersheds. The knowledge about spatial distribution and characteristics of permafrost is essential for understanding the functional relationships between permafrost and other components of the ecosystems. This understanding will be used to improve existing ecosystem models by improving the representation of various feedback mechanisms operating in these systems. This knowledge is also necessary to properly prescribe the initial properties and conditions in these models.

1.1 What controls permafrost and ground ice distribution and sensitivity to climate change? How does the presence of permafrost and ground ice influence geomorphology and the physical response of the land surface to climate change?

Efforts to map and predict present day and future distribution and extent of permafrost and ALT have largely relied on thermal models of varying complexity (Riseborough et al. 2008). Accurate predictions require models having some representation of the influence of topography, water, soil, vegetation, and snow on subsurface thermal conditions (Jorgenson et al. 2010). Process-based thermal hydrology models integrated with observations in Phase 1 have been demonstrated to match observations of active layer and thermal conditions in the subsurface (Atchley et al. 2015) and have been used to quantify the sensitivity of predictions of active layer to uncertainty in subsurface properties (Harp et al. 2015). Simpler thermal models have shown great promise in refining regional predictions of permafrost distributions at increasingly finer spatial resolutions (Panda et al. 2014, Zhang et al. 2014). Additionally, recent empirical models that combine remotely sensed land surface properties (including subsurface geophysics, topographic metrics and climatic variables) have provided 30-m resolution estimates of the probability of near-surface (within 1 m) permafrost for parts of Alaska (Pastick et al. 2014). A goal of NGEE Arctic Phase 2 is to build on these existing methodologies to provide the site level characterization needed for fine-scale permafrost modeling (IM1, IM5, IM6) and to advance efforts to develop regional and pan-Arctic predictions of present day and future permafrost states. These efforts will be aimed at using both field observations and fine-scale models to develop a mechanistic understanding of how geomorphic and other landscape properties control permafrost distribution and its properties such as temperature, ALT, permafrost ice content, and proximity to bedrock. The sensitivity of permafrost to change and degradation is not just a function of temperature but depends strongly on organic layer thickness, vegetation, snow cover, soil saturation and inundation, bedrock, and landscape disturbances (Jorgenson et al. 2010).

Task 1.1A: Develop site and transect-scale permafrost maps based on field data, remote sensing and modeling. We will quantify the interactions between microtopography, vegetation distribution (based on analyses in Q3 and Q4), ALT, the extent and depth of permafrost (where feasible), permafrost and active layer temperatures, and the physical, thermal and hydrological properties of bedrock, soil and the active layer. The quantification of these interactions will be based on subsurface field investigations that include core collection, surface and subsurface temperature measurements, measurement of surface and subsurface thermal properties, soil moisture and inundation patterns (see Q5), and geophysical methods including seismic dataset acquisition. In addition to new data acquisition, this task will also synthesize and collate all existing soil cores and climatological data from published and publicly available datasets for our selected study sites. These datasets will be combined with characterization of topography and topographic attributes, such as slope, aspect, and gradient, and from existing and newly acquired datasets using kite-based and airborne platforms. Using deterministic permafrost models and novel inversion frameworks, point and transect based datasets will be extrapolated to provide site-scale maps of permafrost spatial distribution, depth (were feasible), and occurrence in relation to bedrock, as well as ALT. Characterization of ice content, hydrological properties and development of freeze-thaw constitutive relationships will be aided by laboratory manipulations of field cores. Development of these


permafrost and ALT maps will be closely aligned with vegetation mapping efforts (see Q4) and hydrological investigations (see Q5).

Task 1.1B: Development of a regional-scale ecotype and probabilistic maps of the entire Seward Peninsula. Based on the mapping conducted in Task 1.1A, a high-resolution (30 m) permafrost map reflecting permafrost temperature, ALT, and permafrost vulnerability to climate warming will be established. Datasets developed from site and transect-scale investigations (Task 1.1A) will be integrated with regional datasets of physical properties, glacial history, fire history, topography and existing and newly acquired remotely sensed imagery to scale up the observations and parameterize regional permafrost models such as GIPL (Marchenko et al. 2008). Probabilistic maps will include characterizations of distributions of permafrost, ALT, surface and near surface bedrock occurrence, soil moisture and water table (in conjunction with Q5), vegetation distribution (in conjunction with Q4), and subsurface properties such as organic layer thickness, mineral content, porosity and density.

Task 1.1C: Identify key controls on ALT and permafrost extent, including climate, hydrology (see Q5), evolution of vegetation (see Q4), and rapid change in surface and subsurface landscape structure in order to assess and predict permafrost vulnerability to thaw. In regions at the present day boundary of continuous and discontinuous permafrost, the susceptibility of permafrost to thaw is strongly controlled by local landscape properties. Building on Phase 1 and ongoing numerical experiments of polygon-scale thaw dynamics (Barrow), Phase 2 will focus fine-scale model simulations of how climate and environmental drivers and ALT will alter permafrost thickness and extent at the hillslope scale (Seward Peninsula). Phase 1 results at Barrow and ongoing model development (IM1, leverage IDEAS Use Case 2 development) will be used to understand spatially and temporally how ALT changes as ice-wedge polygons degrade. In the Seward Peninsula, these models and simulations will serve as a starting point onto which additional topographic and land surface property complexity will be added. In more complex terrains with longer and steeper topographic slopes, we will explore the key thermo-hydrological drivers for permafrost change. Exploration of these drivers will be closely coupled with data and predictions regarding changes in vegetation (see Q4) and hydrology (see Q5). The results of these simulations will be integrated with permafrost and land surface characterization efforts conducted in Tasks 1.1A and 1.1B to provide probabilistic maps of permafrost vulnerability under future conditions of changing temperature, hydrology and vegetation cover. Exploration of model inferred permafrost sensitivity to these changes will help to constrain uncertainty associated with projections of future permafrost change. Finally, the outcomes of these experiments will be used to provide a process-informed method for upscaling predictions of changes in carbon available for decomposition, and to provide key inputs for predictions of shrub dynamics (see Q4) and hydrology (see Q5).

Deliverables (including MR5): Site- to regional-scale maps of permafrost extent, ALT, and vulnerability to change. Upscaling approaches for using topography, subsurface properties, and eco-type characteristics to make process-informed predictions of permafrost states at regional to pan-Arctic scales.

1.2 How does the structural arrangement of heterogeneous landscape elements control the watershed to sub-watershed scale characteristics of the landscape and thus ecohydrologic processes and response of the landscape to climate change?

Despite recent advances to move toward a watershed-based representation of the landscape in ALM (Li et al. 2013; Tesfa et al. 2014; Voisin et al. 2013), the heterogeneity and structural arrangement of the landscape is currently not captured in ALM. Field-based studies indicate that the structural arrangement of watersheds directly controls runoff characteristics (Jencso et al. 2009) which in turn strongly influences carbon cycling (Tang et al. 2014) both in terms of DOC fluxes (Pacific et al. 2010) and CO2 efflux (Riveros‐Iregui and McGlynn 2009). Landscape heterogeneity also strongly influences permafrost distribution and vulnerability to change (Shur and Jorgenson 2007). Modeling-based studies suggest that the absence of landscape heterogeneity in model representations of the land surface strongly affects predictions of ecosystem responses in dynamic vegetation models (DGVMs) which in turn affects


predictions of vegetation carbon fluxes and stocks (Pappas et al. 2015). In support of Task IM1, Phase 2 work will be focused on informing and enabling the representation and initialization of the landscape heterogeneity and scale relevant eco-hydrological process across scales within NGEE Arctic multi-scale modeling framework.

Task 1.2A: Classify the landscape into unique eco-geomorphic units using a nested hierarchical approach with multiple gradient inputs such as topographic, vegetation (see Q4), geomorphic, subsurface properties (Task 1.1A) and hydrological attributes (see Q5). Using classification methodology, we will analyze the role of and relationships between nested landscape units across scales to develop effective characterization strategies that will then be used to map these units (Task 1.1B) and parameterize models (Tasks 1.2B and 1.2C). Incorporated into the classification will be the identification of unique geomorphic units such as hillslope, hollow, hilltoes, and floodplains that have unique and discernible evolutionary histories and process controls. Many of these unique geomorphic zones have distinct subsurface characteristics arising from the coupled landscape evolution and carbon cycle dynamics that have shaped the present day landscape in the millennia since these regions were last glaciated. Incorporation of these process-based metrics into the classification effort will aid in the spatial extrapolation of units developed on the Seward Peninsula and Barrow. The classified landscape units will provide explicit and/or parametrized representations of landscape structure and heterogeneities to model grids at both the fine scale (IM6) and intermediate and ESM-scales (IM1).

Task 1.2B: Predict historical and future Alaska landscape transitions caused by thermokarst disturbances by integrating concepts from the Alaska Thermokarst Model (ATM) into ALM. The ATM tracks transitions among landscape units (or cohorts) and is conceptually consistent with the watershed delineation approach being developed in ALM. The transition probabilities can also be improved by using the proposed high-resolution ATS or other fine-scale models, and we propose to do so as they become available. As the probabilities are essentially Reduced Order Models (ROMs) of the underlying high-resolution processes, we will apply our recent NGEE Arctic POD-MM (Pau et al. 2014; Pau et al. 2015) and POD-GPR (Liu et al. 2015) ROM approaches to perform this upscaling.

Task 1.2C: Conduct fine-scale model simulations to understand how variations in landscape features and arrangement impact the parameterization, initialization and dynamics of eco-hydrologic processes across varying scales. Modeling of multiple realizations of landscape configurations will be required to explore the effect heterogeneity has on critical model outputs such as hydrology, permafrost thaw, and carbon cycling. Using the fine-scale models (IM6) we will explore at what scales heterogeneities impact model predictions and how the spatial arrangement of landscape elements influences lateral fluxes across the landscape. In Barrow, where ice wedge polygons are the dominant landscape features, hydrological modeling (Liljedahl et al. 2012) and thermo-hydrological modeling with the ATS demonstrate the arrangement of troughs, rims, and polygon centers, and whether the centers are high or low strongly controls the thermal dynamics and surface water distribution and fluxes. In regions of the Arctic such as the Seward Peninsula where features such as polygons and solifluction lobes range in relief from a few meters to entire hillslopes that are kilometers in scale, fine-scale modeling will be needed to quantify at what resolution and scales geomorphic features will need to be represented in models in order to accurately capture the thermo-hydrological responses needed to predict ecosystem dynamics from the plot to ESM grid scale. For example, a scale-aware representation of hillslope dynamics at a sub-watershed scale may require representation of the effect of solifluction lobes on lateral transport of water, carbon and nutrients. However, at the ESM grid scale, it might be possible to parameterize the integrated influence of solifluction lobes on hillslope processes without explicitly resolving the features.

Deliverables (including MR1): Characterization and model representation of landscape heterogeneity and eco-hydrologic processes to develop process knowledge and parameterization across scales. Synthesize key controls on functional zones in Barrow and on the Seward Peninsula (i.e., as a function of


variable topography, permafrost/bedrock characteristics, vegetation, and vertical/horizontal water flux characteristics).

1.3 What are the controls on the spatial and temporal rates of change of topography, surface and subsurface properties of the landscape in permafrost-dominated environments?

Thaw of permafrost and alteration of the land surface structure can take many forms and occur at highly variable rates (Jorgenson and Osterkamp 2005). These changes might be driven by slow but widespread ‘press’ disturbances, such as increases in air temperature, or they might occur rapidly across a spatially restricted area in the form of ‘pulse’ disturbances such as fire, thermokarst, thermal erosion and gullying (Grosse et al. 2011). Due to both spatial resolution and process complexity, current generation ESMs do not adequately represent or capture the impacts of ‘pulse’ disturbances. The advent of fine-scale thermo-hydrodynamic models, such as the ATS, with the capability to begin to explore surface deformation now offers the potential to numerically explore the feedbacks between permafrost loss and landscape change. On the Seward Peninsula, the breadth of landscapes and a range of rapid landscape changes (thermokarst, lake drainage, gullying and fire) currently occurring and having occurred within the historic past provides an opportunity to understand the controls on rapid change and assess the impact of these changes on the carbon cycle (see Q2), vegetation (see Q3 and Q4), and hydrology (see Q5).

Task 1.3A: Perform analysis of historical imagery to detect areas of rapid change and, where possible, identify drivers for change (e.g., fire, thermokarst, vegetation change, and gullying) and quantify surface and subsurface properties. The use of historical aerial photographs in combination with more recent satellite imagery has proven effective at detecting and quantifying landscape changes (Jones et al. 2011; Swanson 2013) in permafrost environments. We will assemble and analyze historical aerial photographs for the southern Seward Peninsula and compare them to recent aerial photographs and satellite imagery to detect thermokarst, gullying, thermal erosion features, fire occurrence, and vegetation changes (in conjunction with Q4). Areas of detected change will be compared with maps of historic fire occurrence, soil properties, landscape analysis (Task 1.2A) and landscape classification (Task 1.2B) to correlate occurrences of rapid change to a suite of landscape characteristics and possible drivers for disturbance. At these sites soil properties including ALT and ice content, vegetation cover, extent of ground surface disturbance (including erosion) and hydrological conditions will be quantified. For active sites, field surveys of topography will be made to monitor the timing and magnitude of seasonal changes.

Task 1.3B: Use a combination of simplified landscape evolution and fine-scale thermo-hydrological models to quantify the relative role of changes in climate, surface erodibility (closely associated with vegetation cover and disturbance), loss of ground ice, soil properties, hydrology and slope on susceptibility of the land surface to rapid change. Increases in seasonal thaw depth, changes and loss of vegetation cover, and reorganization of flow path structure all have the potential to drive changes in the storage and lateral flux of carbon, sediment, and nutrients across the landscape. Reduced complexity two-dimensional landscape evolution models parametrized along field transects and sub-watersheds will be used to examine how changes in hillslope soil transport processes and surface erosion may respond to warming and changes in hydrology and landcover. In these simplified models, the impact of landsurface and subsurface changes may be parametrized to capture geomorphological responses at the scale of hillslopes and watersheds. For example, the deepening of the seasonal thaw depth will result in a thicker mobile soil zone, and a loss of vegetation may be represented by locally increasing the erodibility of the ground surface. In concert with the landscape evolution modeling, fine-scale thermo-hydrological models (IM6) will be used to conduct a series of idealized simulations based on field observations to quantify what combination of surface and subsurface landscape properties lead to rapid changes, what landscape configurations appear inherently stable, and what settings have strong resilience/recovery in response to local disturbance. For example, the fine-scale models may be used to investigate how variable ice content along a hillslope transect may lead to local ground subsidence, which in turn will locally increase the ground surface slope. These results could then be used in the landscape evolution models to explore the


potential feedbacks on soil movement and surface erosion. A particular focus of the modeling will be to understand under what conditions these local disturbances may expand over larger areas.

Deliverables: Probabilistic maps of land surface vulnerability to change. Metrics for change may include change in ALT, extent of permafrost and its position to top of bedrock, subsidence/thermokarst, erosion, drainage integration, and vegetation changes. These maps will serve to test predictive capabilities of model to identify regions of rapid change and guide future site selection.

QUESTION 2. WHAT WILL CONTROL RATES OF CO2 AND CH4 FLUXES ACROSS A RANGE OF PERMAFROST CONDITIONS?

Robust predictions of a warmer Arctic and thawing permafrost forecast a substantial increase in the amount of SOM in the active layer (Mishra and Riley 2012). This thawing underscores the importance of addressing uncertainty in how environmental factors will mediate feedbacks among Arctic soil warming, microbial community changes, SOM decomposition, and resulting changes in GHG fluxes (Schuur et al. 2013; Treat et al. 2015; Koven et al. 2015a). Together with potential changes in the thaw season length, soil moisture, and vegetation distribution, permafrost thawing poses the challenges of predicting the extent and rate of long-frozen SOM decomposition and the proportions of CO2 and CH4 emissions.

Four factors interact to regulate decomposition of organic matter previously frozen in permafrost soils and to produce an emergent GHG flux (Figure 16). First, the quantity of SOM that is thawed, its physico-chemical structure, and its interactions with minerals shape the potential rate of GHG production (Conant et al. 2011). Second, the hydrological, geochemical, and thermal properties of the newly thawed soil constrain the accessibility of organic matter, nutrients, and electron acceptors (Hobbie et al. 2000). Third, active microbial communities and their enzymes catalyze most organic matter decomposition reactions and determine the mixture of gases and dissolved organic matter (Schimel and Schaeffer 2012). Finally, transport processes through plants or soil control the rate and mixture of GHG emissions in methane producing regions (Bridgham et al. 2013).

Section 5A (Modeling and Scaling Strategy) describes the ALM-PFLOTRAN framework for coupling thermal-hydrology and subsurface biogeochemistry to improve predictions of net carbon exchange in Arctic ecosystems. Task IM2 in that section will initiate simulations at intensive study sites, setting a baseline for improvements in parameterization and model structure described here. Integration with process-rich NGEE Arctic models that specifically represent microbial activities in soils will be key to advancing Phase 2 research priorities on fine-scale modeling (Wang et al. 2012; Tang and Riley 2015). Here we describe three subquestions with associated tasks that address large uncertainties in predicting Arctic SOM decomposition and resulting changes in GHG emissions. Field and laboratory campaigns will interrogate factors that regulate decomposition by juxtaposing the highly organic, continuous permafrost soils of Barrow with discontinuous permafrost soils on the Seward Peninsula. Measurements and experiments will be informed by mechanistic models and designed to parameterize, enhance, and test those models through collaborations between experimental and modeling teams.

Figure 16. Controls on permafrost organic matter degradation and greenhouse gas emissions.


2.1. How do temperature, moisture, mineralogy, geochemistry, and redox processes affect CO2 formation and CH4 production and oxidation rates?

Abiotic factors strongly influence the decomposition of thawed SOM (Schmidt et al. 2011). Current biogeochemistry models include temperature and soil moisture as key controls on decomposition, GHG production, and emission rates (Oleson 2013). In addition, process-rich models developed in Phase 1 include a growing set of abiotic factors that affect decomposition and GHG emissions (Tang and Riley 2015; Bouskill et al. 2014). Organic-mineral interactions, the activities of extracellular soil enzymes, and soil properties such as electron acceptor availability for anaerobic respiration are all potentially controlling variables in carbon cycle processes. Observations are needed, however, to evaluate the relative importance of these mechanisms in the contrasting environments of Barrow and the Seward Peninsula and to parameterize these mechanistic models. At Barrow and Seward Peninsula sites, we will combine field observations and laboratory studies to determine how soil and air temperature, surface vegetation, hydrology, mineralogy and geochemistry control microbial and enzymatic decomposition rates. Measurements of dissolved inorganic species and isotopes in soil water performed in Q5 tasks will be closely coordinated to examine the release of nutrients and electron acceptors from thawing permafrost. Decomposition studies will be coordinated with tasks in Q3 to span SOM transformation from new plant litter to newly thawed permafrost OM.

Task 2.1A. Organo-Mineral Interactions. SOM associations with minerals affect its accessibility for microbial degradation (Torn et al. 1997; Mikutta et al. 2006; Kleber et al. 2005). Furthermore, certain OM components are preferentially sorbed, influencing decomposition pathways and rates (Gu et al. 1995; Vázquez-Ortega et al. 2014). Critical questions with regard to feedbacks in the Arctic include where and when organo-mineral interactions will slow decomposition in the organic and mineral horizons of the active layer or newly thawed permafrost (Hobara et al. 2013). We will examine SOM adsorption/desorption in soils from Barrow and Seward Peninsula under field-relevant laboratory conditions to determine the biodegradation rates of the sorbed and aqueous SOM by measuring the decomposition products from incubations with microorganisms. These experiments will also determine how temperature, moisture, and geochemical conditions influence these interactions. Specific minerals can also be used in controlled experiments to discern the sorption and desorption mechanisms using advanced spectroscopic techniques [e.g., FTIR and high-resolution mass spectrometry (HR-MS)] (Mann et al. 2015; Hodgkins et al. 2014; Herndon et al. 2015). The rates and pathways of SOM degradation can be used to parameterize and validate mechanistic models of soil carbon dynamics (Wang et al. 2012; Tang and Riley 2015).

Task 2.1B Extracellular enzyme activities. Extracellular microbial enzymes catalyze the decomposition of carbohydrates and proteins in Arctic cryosols and permafrost (Wallenstein et al. 2009, Burnset et al. 2013). The short Arctic thaw season and freezing soil temperatures are proposed to limit these decomposition reactions (Hugelius et al. 2014). Therefore, it is important to understand these enzymes’ sensitivities to temperature, water content, pore water chemistry and interactions with soil minerals to accurately model SOM decomposition rates (Allison, et al. 2010; Tang and Riley 2013; Tang and Riley 2015). We will use high-throughput colorimetric and fluorescence laboratory assays to measure the activities of endogenous soil enzymes that break down cellulose, hemicellulose, pectin and proteins at low temperatures and field-relevant water contents (Wallenstein and Weintraub 2008; Steinweg et al. 2012; Dick 2011). Enzyme inactivation or stabilization by adsorption to soil mineral particles will be tested by the addition of relevant mineral sorbents (Zimmerman and Ahn 2011). We will also examine the competition between enzymes and SOM for mineral sorption and interactions at varying temperatures (Allison 2006). Rates and temperature sensitivities of hydrolytic soil enzyme activities at field-relevant temperatures and their interactions with soil mineral particles will be measured to parameterize and refine highly mechanistic models of SOM decomposition. We will model the ecology of microbial decomposition by quantifying at least two or three specific guilds (bacterial and fungal) and identify and parameterize the main traits associated with each heterotrophic strategy (Allison 2012; Krause et al. 2014;


Crowther et al. 2014) before incorporating this information into models such as the recently described N-COM (Zhu et al. 2015).

Task 2.1C Soil moisture characteristics. Models of thermal hydrology and soil biogeochemistry need to predict soil moisture and oxygen availability (Koven et al. 2013b, Oleson et al. 2013). In unsaturated soils, the soil moisture characteristic (or soil moisture retention curve) relates the soil’s suction properties to the proportion of pore space filled with water. This relationship describes how tightly Arctic soils hold water, which is key to predicting unfrozen water content (Painter and Karra, 2014, Rawlins et al. 2013), decomposition rate (Oleson et al. 2013), plant soil water stress, and CH4 oxidation potential (Gulledge and Schimel 1998). These data are rare for Arctic or for organic-rich soils more generally (Hinzman et al. 1991). Substantial differences in the soil type, organic composition and resulting porosity at increasing depth in Arctic soils creates uncertainty in hydrology and biogeochemistry models (Beringer et al. 2001, Tuller and Or, 2005). In coordination with Q5, we will compile a data series of soil moisture characteristics from active layer and permafrost horizons at Barrow and Seward Peninsula sites. We will use a tensiometer system (UMS/Decagon Devices) to measure soil moisture characteristics in the laboratory for soils at increasing depth from thawed core samples and column experiments (Whalley et al. 2013). The measurements will be correlated with laboratory-bases estimates of methanogenesis and methane oxidation from incubation experiments. Soil moisture characteristic curves are also key inputs to permafrost thermal hydrology models. The new curves will also be used in the baseline watershed simulations that will be developed in Task IM5 and used to support multiple science questions.

Task 2.1D Contrasting site biogeochemical controls. In addition to representing end members of permafrost degradation, Barrow and Seward Peninsula soils differ in temperature, length of growing season, precipitation, parent material, and topography (Benning 2005, Jenny 1941), leading to differences in the abundance of electron acceptors such as iron(III) (Lipson et al. 2010), vegetation composition, and many other factors. We will construct and sample site-pairs between current Barrow sites and new sites on the Seward Peninsula, to compare the composition and flux of CO2, CH4 and N2O produced and emitted to assess geochemical and hydrological influences on GHG emissions, and how well our models reproduce these patterns. We envision three sampling events at locations covering the range of soil moisture regimes at each site, during the early, mid and late thaw season to compare how GHG production varies through the season and over several field seasons at Barrow (Throckmorton et al. in press, Smith et al. in preparation, Herndon et al. in review). Surface fluxes of CO2 and CH4 will be measured in the field using chambers (Lara et al. 2015, see Q4 for eddy covariance measurements). Soil gases and gases dissolved in soil water will be sampled at depth from drive-point samplers and piezometers (Pries et al. 2014, Smith et al. in preparation, Newman et al. 2015, Herndon et al. in review). Frozen soil cores will also be collected for laboratory experiments and analyses of microbial communities at the different sites. Geochemical and geophysical measurements will be coupled to microbial community structure and composition (Tas et al. in preparation). Rates and compositions of gases produced at field-relevant conditions will be measured using laboratory incubation experiments (Roy Chowdhury et al. 2015). Gas production and emission measurements will be compared with geochemical measurements of redox-active soil species to partition gas production among possible pathways.

Deliverables (including MR8): Quantify the magnitude and sensitivities of key environmental controls on the SOM degradation and mineralization processes in cryosols that will determine future GHG emissions. The process rates, fluxes, and response factors will enhance model predictions by the ALM-PFLOTRAN biogeochemistry framework as well as separate mechanistic models.

2.2. How will changes in the length of the thaw season and rate of soil thawing affect CO2 and CH4 emissions?

A longer thaw season could lead to profound changes in GHG production rates and surface flux from Arctic permafrost soils (Olefeldt et al. 2013, Koven et al. 2015a). Together with warmer temperatures, prolonged thawing will melt ground ice, releasing OM from permafrost into a deeper active layer. The


potential concurrent increase in soil drying (see Q5) due to drainage, evaporation or changes in plant communities (see Q3 and Q4), may increase oxygen diffusion thereby increasing aerobic respiration and CO2 emissions. Drainage could also increase the lateral transport of OM, potentially creating hotspots for GHG emissions. Alternatively, earlier or more rapid thaw may lead to ground subsidence and periods of inundation that favor early summer CH4 production if (1) drainage and evapotranspiration do not keep pace with the rate of melting ground ice, (2) methane is oxidized more slowly than it is produced and transported to the surface or (3) there is a non-linear increase in methanogen population size that supports increased CH4 production. These counterbalancing effects create substantial uncertainties in the mode and timing of GHG emissions that need to be investigated using coordinated simulations and measurements described here. These uncertainties will be addressed through in situ measurements of variation in soil moisture and temperature, microbial community activity, and GHG fluxes and isotopes (13C, 14C) complemented by targeted laboratory experiments designed to determine key factors affecting CO2 and CH4 emissions during the annual thaw period. These data will be used to inform both ALM-PFLOTRAN and process-rich, fine-scale models of potential changes in GHG fluxes from Arctic soils under changing conditions.

Task 2.2.A Micro-warming experiment. Observations show that Arctic GHG fluxes and SOM decomposition rates increase with increases in Arctic temperatures (Lara et al. 2015). However, due to the complexity in SOM structure, microbial community interactions, and plasticity in microbial traits, predicting temperature response of microbial SOM decomposition has proven to be difficult. To better understand the controls on carbon cycling under a warmer climate we will deploy in situ soil warming and priming experiments. We will study decomposition of native SOM and added degradable compounds, like glucose. These experiments are designed to quantitatively investigate how soil biotic and abiotic properties affect soil carbon cycling and investigate how these properties change with depth (organic and mineral ), plant traits, carbon inputs, and soil warming. We are currently testing prototypes of the micro-warming manipulation in Barrow as part of Phase I. Based on an approach pioneered by other DOE TES projects, we are placing a resistance cable vertically into the center of a 25 cm-diameter PVC mesocosm that was inserted 50 cm into the tundra in 2014. The test plots will be monitored for soil temperature, moisture, and CO2 production and sampled and analyzed for thermal conductivity, soil CO2 isotopes, and microbial community composition.

Task 2.2B Synthesis of thaw season dynamics. Seasonal trends in the rates of GHG emissions from Arctic soils depend on more than instantaneous soil temperature and water contents (Roy Chowdhury et al. 2015, Sturtevant et al. 2012, Song et al. 2012, Pickett-Heaps et al. 2011). There is evidence for bursts of CH4 emitted during the early spring thaw (Raz Yaseef et al. in preparation) or the onset of freezing (Mastepanov et al. 2008), and plant phenology plays a large role in seasonal trends of carbon cycling. It is not clear whether current models accurately represent the dynamics of microbial activities during the thaw season. We will prepare a synthesis of multi-scale data collected from this project and other sources to evaluate the magnitude and distribution of thaw season emission trends. These results will be used to inform and validate dynamic models of GHG emissions.

Task 2.2C Ground surface subsidence effects. Soil subsidence (e.g., thermokarst) induced by ground ice melting and permafrost thaw drives topographic evolution in the permafrost region and associated changes in hydrology, vegetation, soil properties and microbial activities. While both regional and local scale soil subsidence have been studied and modeled (Liu et al. 2012, Shiklomanov et al. 2013, Levis et al. 2012), only a few studies explored how this process directly impacts the production, transport, and release of GHGs (e.g., Hodgkins et al 2014). Laboratory studies will use controlled soil core thawing and re-freezing to measure the short-term impact of ground ice melting on soil subsidence and subsequent changes in hydrology, microbiology and GHG production and release. The long-term effects will be evaluated by comparing areas with different levels of subsidence at both the Barrow and Seward Peninsula sites.


Task 2.2D Lateral transport of SOM. Topography and rain or snowmelt drive the movement of water along with dissolved organic matter (DOM), causing its fractional sorption, desorption, and re-distribution on time scales ranging from hours to decades (Kawahigashi et al. 2006). Fractionation or compositional changes of SOM during sorption and transport are common due to presence of thousands of individual DOM molecules (Vázquez-Ortega et al. 2014, Gu et al. 1996, Mann et al. 2015). New measurements are required to understand how deeper active layers, warmer soils, and longer thaw seasons affect the role of lateral transport and redistribution, which could create GHG emission hospots or pulses. We will conduct plot-scale experiments at a Seward Peninsula site, where a number of piezometers will be inserted to the permafrost boundary, allowing periodic pore water sampling and analysis (Newman et al. 2015, Herndon et al. 2015b). DOM characteristics and compositional changes will be monitored using techniques described in Task 2.1A, and then compared with the rates of CO2 and CH4 production as affected by the redistribution. Controlled experiments may also be performed in laboratory column flow-through systems where additional biogeochemical parameters (e.g., microbial community, pH, ionic composition, and mineralogy) can be investigated. Results from these studies will be used to parameterize coupled reactive transport models and identify landscape features with disproportionate impacts on GHG emissions.

Deliverables: Improved estimates of the spatial and temporal changes in SOM degradation and GHG emissions from a longer projected thaw season and increased melting of ground ice. Field measurements will help to predict GHG emission hotspots and pulses that are outliers within the subgrid area.

2.3. What are the most important controls on decomposition rates of permafrost organic matter, and what will be the future rates of CO2 and CH4 production from this currently frozen material?

Permafrost protects SOM from microbial decay (Zimov et al. 2006, Hugelius et al. 2014). In simulations that include permafrost thaw, one of the largest uncertainties in future Arctic CO2 fluxes is the decomposition rate of currently frozen SOM (Schuur et al. 2013). Although synthesis studies and NGEE Arctic radiocarbon evidence suggest that older SOM is degraded after thawing, the proportion of permafrost-protected OM that microorganisms can readily degrade is not well known (Schuur et al. 2008, Ciais et al. 2013). The state of the art for modeling subsurface SOM decomposition is a single global parameter for decreasing the nominal decay rate with depth (Koven et al. 2015b), which has not been tested for the Arctic. Research priorities emerging from these studies include (1) determining the amount of SOM that will be newly thawed with time; (2) projecting deep soil moisture; and (3) quantifying the controls on decomposition of this newly thawed SOM, which is the focus of this subquestion. We will employ a continuum of spectroscopic and biochemical characterizations of the frozen SOM, laboratory incubations, whole-core manipulations, and isotopic analysis to establish these linkages among factors controlling permafrost OM degradation.

Task 2.3.A Transplant experiments. Microbial community responses to climate mediated changes in permafrost physiochemical properties and to increased availability of permafrost OM are not well understood and consequently not represented in models (Jansson and Taş 2014). To overcome this bottleneck, primary factors (e.g., soil moisture, salinity, and redox conditions) controlling GHG production rates across a range of permafrost samples from Barrow and Seward Peninsula sites will be characterized and quantified via laboratory experiments. These experiments will be complemented with in situ measurements of permafrost OM mineralization. We will perform highly replicated reciprocal field transplant experiments where sections from upper permafrost layers will be moved to active layer (mineral horizons). We will follow changes in SOC, hydrology, soil chemistry and microbial responses (community structure, activity and functions). Comparisons between laboratory and in situ field measurements will inform potential biases associated with soil disruption commonly found in incubation studies.

Task 2.3.B Predictors of permafrost OM decomposition. Both the physico-chemical protection of SOM and its chemical structure affect decomposition rates (Conant et al. 2011). We will use


spectroscopic and enzymatic saccharification methods to assess permafrost OM composition and estimate decomposition rates, using aerobic and anaerobic laboratory incubation experiments and field transplant results to assess the predictive power of the following measurements. We will utilize HR-MS at EMSL (Mann et al. 2015), FTIR, and 2D excitation-emission fluorescence to interrogate chemical and compositional properties of the permafrost carbon from Barrow and Seward Peninsula sites (Chen et al. 2003, Rinnan and Rinnan 2007). Additionally specific low-molecular weight organic structures including organic acids, alcohols, and reducing sugars will be examined using established gas and HPLC chromatographic techniques. We will also adapt enzymatic saccharification methods that are widely used in the biomass deconstruction field to assess the amount of polymeric carbohydrate (cellulose, hemicellulose and pectin) and protein that is accessible to enzymatic degradation (Bünemann 2008, Roche et al. 2009). This mixed-enzyme saccharification assay is not expected to hydrolyze all of the soil carbohydrates and proteins, rather the quantity and composition of enzymatic hydrolysate are expected to be good predictors of the rate and extent of SOM mineralization in parallel incubation experiments (Nadeau et al. 2007). Results from these experiments will be used in mechanistic models of microbial-enzyme dynamics to characterize the potential rate and extent of decomposition for permafrost OM.

Deliverables: Improved understanding of the potential for currently perennially frozen SOM to be decomposed and the short- and long-term changes in GHG production due to thaw, as a function of, SOM composition and physical-chemical state, climate,, and microbial community response to thaw.

QUESTION 3. HOW WILL WARMING AND PERMAFROST THAW AFFECT ABOVE- AND BELOW-GROUND PLANT FUNCTIONAL TRAITS, AND WHAT ARE THE CONSEQUENCES FOR ARCTIC ECOSYSTEM CARBON, ENERGY, WATER, AND NUTRIENT FLUXES?

Models currently aggregate the global diversity of plant traits into plant functional types (PFTs) that share similar structural and functional characteristics (Reich 2014, Wullschleger et al. 2014). However, simplifying assumptions made to reduce variation in the structure and function of plant communities can limit the ability of models to accurately project vegetation responses to changing environmental conditions, now and in the future (Fisher et al. 2015, Fisher et al. NGEE-Tropics trait-based modeling white paper). It has long been recognized that plant traits and strategies strongly predict plant function, including growth, survival, and capture of limiting resources (Grime 1974, Reich 2014). In the Arctic, aboveground traits such as Vc,max, leaf spectral properties, vertical scaling of leaf traits through the canopy, the complex structure and height of woody species, and the insulating properties of moss determine maximum photosynthetic rates, influence land surface albedo, control the local distribution of snowpack, or facilitate permafrost persistence (Epstein et al. 2004a, Sturm et al. 2005, Soudzilovskaia et al. 2013, Rogers et al. in preparation). Belowground traits such as mycorrhizal association, capacity for N fixation, the relative allocation of carbon to fine roots, and the distribution of fine roots throughout the soil profile, can affect plant nutrient acquisition, the timing and release of CO2 and CH4 to the atmosphere, and soil carbon and nutrient storage under current and future environmental conditions (Cornelissen et al. 2001, McKane et al. 2002, Myrold and Huss-Danell 2003, Mack et al. 2004, Iversen et al. 2015). These suites of plant characteristics are filtered and shaped by current and future environmental conditions, resulting in competitive exclusion and gradual shifts in plant community composition and structure that ultimately drive important ecosystem processes (e.g., Epstein et al. 2004a, Mack et al. 2004, Euskirchen et al. 2009, Hartley et al. 2012, Pearson et al. 2013). Model representation of plant functional diversity is advancing using a number of alternative approaches to move away from fixed, invariant traits to probability density functions that span trait variance and covariance. These approaches include trait-environment linkages (Ali et al. 2014, Reich 2014, van Bodegom et al. 2014), optimality approaches (Xu et al. 2012, Medlyn et al. 2011), trait filtering (Fisher et al. 2012), and adaptive dynamic global vegetation models (DGVMs, Schieter et al. 2013). Common to all these approaches is in need for richer descriptions of plant traits in the Arctic, including trait plasticity and covariance, acclimation, spatial and temporal dynamics, and trait variation in response to environmental drivers that


will allow next generation Arctic PFTs respond to changing environmental conditions (Figure 17; e.g., Pearson et al. 2013, Euskirchen et al. 2014).

3.1 What are the key above- and below-ground plant functional traits that determine carbon, energy, nutrient and water fluxes?

The parameters used to describe one or a few Arctic PFTs in many ESMs do not accurately represent Arctic vegetation (reviewed in Wullschleger et al. 2014). Even model parameters associated with processes thought to be relatively well understood (e.g., photosynthesis) are highly uncertain, especially for understudied high-latitude ecosystems (Bonan et al. 2011, Rogers 2014, Rogers et al. in preparation), and representations of Arctic plant root structure and function are perhaps even more uncertain, or are missing from current model structures (Iversen et al. 2015). To improve model representation of key Arctic plant functional traits, we will quantify the variation in above- and belowground plant functional traits, identify the major axes of plant trait variation that define differences among PFTs, and develop linkages between plant functional traits and edaphic and environmental conditions. We will parameterize Arctic PFTs with data from Arctic plants and also replace inappropriate, static, parameterization in current models with trait distributions across probability density functions. Improved understanding of the variation in Arctic plant functional traits will enable us to define the number of PFTs necessary (e.g.,

Figure 17. Current models represent Arctic vegetation with just one Plant Functional Type (PFT) and plant traits within that PFT are represented with a static mean value. NGEE Arctic trait-enabled models will add additional PFTs. to represent the diversity of Arctic vegetation and represent their plant traits with probability density functions that describe trait variation.


Chapin et al. 1996) to appropriately capture trait variation in the next generation of trait-enabled models. Our advances in mechanistic understanding and scaling of plant functional traits in the Arctic will be used to reduce the uncertainty in model parameterization of trait distributions associated with newly-introduced Arctic-specific PFTs.

Task 3.1A: Characterizing variation in key Arctic plant functional traits. Using a suite of approaches that include field and laboratory studies, we will quantify variation in above- and belowground plant functional traits across gradients of tundra that encompass thermokarst, landscape topography, active layer thickness, and fire history. We will characterize the variation in structural (e.g., root depth distribution, shrub allometry, and leaf and leaf litter chemical and spectral properties) and functional (e.g., physiological and biochemical parameters associated with CO2 assimilation and uptake of nutrients) traits of the dominant plant species and those which represent different PFTs on the landscape. Further, we will develop new relationships between key above- and belowground traits (e.g., Freschet et al. 2010) to allow prediction of belowground functional traits from more easily observed aboveground traits and evaluation of trait-trait tradeoffs. Our focus will be to measure traits that will reduce uncertainty in model representation and evaluation of Arctic ecosystem processes. As a benchmark for model prediction, we will assess plant community biomass and nutrient distribution, above- and belowground.

Task 3.1B: Developing trait-environment relationships. To investigate trait-environment linkages, we will make environmental measurements in the same locations as our trait measurements. Associated with each plant community subsampled for traits, we will assess the sources and distribution of soil C, N, and P throughout the soil profile and plant-available NH4, NO3, and PO4 in the rhizosphere, as well as soil pH, moisture, and temperature. Co-located measurements of soil order and suborder, permafrost extent, active layer thickness, snow depth, and CO2, nutrient, and water fluxes will be completed in collaboration with other tasks.

Task 3.1C: Harnessing plant trait variation with trait-enabled models. We will build key trait-trait and trait-environment relationships from data collected in Tasks 3.1A and B using empirical statistical models (e.g., Ali et al. 2015) or optimization models (e.g., Xu et al. 2012). We will aggregate observed plant trait variation into the fewest number of PFTs by identifying the major axes of plant trait variation that define differences among PFTs (e.g., using trait covariances among leaves, wood, and roots; Iversen et al. 2015, Reich 2014, Xu et al. 2012). Several co-evolving and competing sub-PFTs will be used to represent trait distributions within a PFT. These relationships will be incorporated into fine-scale and climate-scale models (Task IM3) to simulate C, energy, water, and nutrient fluxes at the measurement sites. The model simulations will be evaluated against observations of plant community biomass and nutrient distribution, above- and belowground, and soil C, N, and P.

Deliverables (including MR3): Measurement of variation in key above- and belowground Arctic plant structural and functional traits that are poorly represented or missing from current model structures to inform and evaluate new model representations of Arctic plant functional traits. Define new Arctic PFTs that capture the key variation in plant traits, above- and belowground, and simulate their impact on ecosystem carbon, energy, nutrient and water fluxes.

3.2 How do traits vary in space and time, and in response to changing environmental conditions?

Here we build on the work in Task 3.1 and leverage the fact that structural and functional properties of plants are reflected in the optical characteristics of plant leaves and canopies to invert remotely-sensed data in order dramatically increase our capability to measure plant traits, and how they vary over space and time (Serbin et al. 2012, Serbin et al. 2014, Serbin et al. 2015, Banskota et al. 2015, Singh et al. 2015). This will enable us to describe trait distributions with probability density functions that reflect variation of traits across much broader spatial and temporal scales than those possible in Task 3.1. Temperature is one of the most important environmental factors controlling physiological processes, yet current ESMs often have a single temperature response function that is used to represent key processes in all PFTs (Oleson et al. 2013) and in some cases the same temperature response function is used to


represent several biological processes (Medvigy et al. 2009). The temperature response of physiological processes such as photosynthesis and respiration has rarely been characterised for high latitude species, particularly in the high Arctic, and typically studies investigating temperature responses have focused on net photosynthesis and not the underlying biochemical mechanisms that drive model responses (e.g., Sendell et al. 2015). We will measure temperature response functions for Vc,max and Jmax and leaf and root respiration in Arctic PFTs to provide models with Arctic specific temperature responses for the model parameters that drive these key processes, rather than emergent states (e.g., photosynthesis). There is currently poor model representation of acclimation of photosynthesis and respiration to elevated growth temperatures (Smith and Dukes 2013, Kattge and Knorr 2007). However, projected, large increases in temperature in the high Arctic may lead to mean leaf temperatures that exceed current photosynthetic temperature optima, potentially impacting carbon acquisition, but also growth and survival of given PFTs (Sage et al. 2008). Therefore, understanding the potential for acclimation of species to novel temperatures is critical for projecting future carbon exchange and potential shifts in community composition. In challenging Arctic ecosystems passive warming is one of the few options for elevating temperature in field enclosures but current approaches to passive warming are unable to achieve the larger temperature elevations that are required to replicate projected temperature increases in the Arctic (Marion et al. 1997; Melillo et al. 2014; Lewin et al. in review). We will use a new technology to elevate air temperature in field enclosures to enable investigation acclimation of photosynthetic and respiratory temperature response functions to the large temperature increases projected for the Arctic (Lewin et al. in review).

Task 3.2A: Enabling the use of remote sensing to scale Arctic plant traits through space and time. To enable temporal and spatial scaling we will test whether relationships between plant structure and functional traits and their spectral signatures (e.g.. Vc,max, leaf N) established in temperate and boreal zones are valid for Arctic plants. This activity is closely linked to Task 3.1A in that spectra will be measured at the same time as the traits. We will use the links between hyperspectral signatures and above- and belowground traits to scale from leaf- and canopy-level traits to larger spatial scales and with greater temporal resolution, using tram, UAS, airborne-, and space-borne remotely sensed data. Ultimately this scaling approach can be used to provide descriptions of Arctic PFTs that are spatially and temporally dynamic that will enrich parameterization of plant functional traits in next-generation model frameworks and validate emergent model states.

Task 3.2B: Temperature response and acclimation of key plant traits. We will measure temperature response functions of key plant traits and the potential acclimation of these temperature response functions to elevated growth temperatures. We will build on work initiated in Phase 1 to produce temperature response functions for Arctic plant physiological process (e.g., Vc,max, Jmax and root respiration). Moreover, we will investigate the potential for thermal acclimation of these key functional traits through the use of zero power warming (ZPW) field enclosures (Lewin et al. in review) coupled with snow management (e.g., Natali et al. 2014) to elevate plant temperature and increase active layer thickness. The ZPW chambers are field enclosures that use solar energy and a passively controlled venting mechanism to elevated internal air temperatures by up to 8°C (they do not require power). We plan to validate expected performance of the ZPW chambers in Barrow in 2015 and 2016. Use of this technology to investigate acclimation in Phase 2 is contingent on the success of this prototyping activity.

Task 3.2C: Evaluating trait-enabled modeling. The ability of the trait-enabled model developed in 3.1C to capture the dynamic variation in plant traits will be evaluated against our observations of the spatial and temporal variation in plant structural and physiological traits (Task 3.2A). To account for the potential for trait acclimation to future warming, we will incorporate the thermal acclimation of key plant traits in our trait-enabled model using data from Task 3.2B. The importance of dynamic traits at large scales will be assessed by comparing trait-enabled model predictive capacity with that of models using PFTs represented only by static traits. The trait-enabled models will also be used to project the effect of plant functional trait responses to a changing climate (e.g., the combined effects of rising temperature and


elevated CO2 on gross primary production) and to future environmental conditions (e.g, changes in rooting depth distribution with active layer thickening).

Deliverables: We will build a suite of spectra-trait relationships that will enable the use of near surface, airborne and remotely sensed data to map above- and belowground temporal and spatial trait space. We will determine temperature response functions for key above- and belowground plant functional traits associated with photosynthesis, respiration and their potential to acclimate to elevated temperatures. Finally, we assess the impact of rich, dynamic trait descriptions in fine- and climate-scale trait-enabled models.

3.3 How do we guide measurement and improve representation of plant functional traits in next-generation models?

Process models encapsulate our best understanding of terrestrial ecosystems and the connection between vegetation structure and function (i.e., plant traits). Improving the predictive capacity of our model projections is a critical motivation of NGEE Arctic, and as such, we propose a formal iterative ModEx approach to facilitate critical feedbacks between data synthesis activities, new plant trait observations, and model improvements described in Tasks 3.1C and 3.2C. Specifically, we will use uncertainty quantification (UQ) and variance decomposition (VD) tools (LeBauer et al. 2013, Dietze et al. 2014) to address uncertainties in model structure and parameterization in order to guide our understanding of which plant traits are driving model uncertainty, prioritize synthesis and data collection to capture trait diversity within an evolving NGEE Arctic PFT set, as well as assess the impacts of model updates and new observations to overall model predictive capacity. This proposed approach differs from a simple model sensitivity analyses (Cariboni et al.2007), which can be misleading as a sensitive parameter that is well constrained by data may exert less influence than poorly constrained, but less sensitive, parameters. Also, a focus solely on parameter uncertainty can be misleading if the focus is on plant traits that may only have a few observations (i.e., are poorly constrained by data), but that also display a negligible impact on overall model output uncertainty. Combining UQ and VD approaches within an interactive ModEx framework allows for the partitioning of model variance into parameter sensitivity or uncertainty, or both. Ultimately this approach will enable us to assess the contribution of new plant trait measurements on our ability to inform model parameterization and representations as well as the impacts on overall predictive uncertainty in key model outputs of carbon, water, energy, and nutrient fluxes, guide new measurements, and assess model updates.

Task 3.3A. Uncertainty Quantification and Variance Decomposition for improving model representation of plant traits. This task will identify and prioritize research efforts to maximize our ability to reduce model uncertainty in the representation of plant traits and efficiently parameterize new Arctic PFTs. In the tasks described above (Tasks 3.1 and 3.2) we will focus measurements on known critical gaps in process representation and the key traits needed for model parameterization identified by NGEE Arctic scientists. We will leverage ongoing plant trait measurements across key gradients in order to capture the observed trait diversity, incorporate this information, and examine model outputs. Here we will leverage existing and emerging NGEE Arctic datasets as well as investments in NGEE Tropics UQ/VD tool development to determine what plant traits and representations continue to dominate uncertainty in key model outputs and establish a baseline for model improvement and additional data collection. The UQ/VD framework will be used iteratively throughout the project as we incorporate new observations and model representations to optimize PFT parameterization and identify additional data needs as the development of an Arctic PFT suite evolves.

Deliverables: An iterative identification of key drivers of model uncertainty, and prioritization of new plant trait measurements that will inform development of the new NGEE Arctic model representation of Arctic PFTs.


QUESTION 4. HOW WILL SHRUB DISTRIBUTIONS CHANGE AND GENERATE CLIMATE FEEDBACKS WITH EXPECTED CLIMATE WARMING IN THE 21ST CENTURY?

Feedbacks involving shifts in the distribution of Arctic vegetation contribute large uncertainty to future climate projections because most ESMs do not have true dynamic vegetation and none has Arctic specific representations (Jian et al. 2014, Zhang et al. 2014, Chae et al. 2015). Contemporary observations in the Arctic show increased shrub growth and colonization (Figure 18) with regional warming (Sturm et al. 2001a, Tape et al. 2006, Myers-Smith et al. 2011, Frost et al. 2014), and observations and models suggest that as global climate warms, shrubs may again become more widespread pan-Arctic (Euskirchen et al. 2014, Buntgen et al. 2015). Compared to low-statured tundra vegetation, shrubs have strong effects on climate through both biophysical and biogeochemical processes, including differences in albedo, carbon storage, energy and water fluxes, N fixation and N cycling (Chapin et al. 2005, Eugster et al. 2000, Gibbard et al. 2005, Swann et al. 2010, Cahoon et al. 2012, DeMarco et al. 2014, Sturm et al. 2001b, Berner et al. 2013, Blok et al. 2015, Weintraub and Schimel 2005, Hiltbrunner et al. 2014, Shaftel et al. 2012, Thomas et al. 2007).

Future climate feedbacks depend on how quickly vegetation patterns change in response to regional warming. Predicting such changes is difficult, however, because they are not simply a function of shifts in the climate envelope of suitable temperature and moisture; expansion is influenced by interacting factors, including dispersal, recruitment, soil temperature, hydrology, biogeochemical cycling, edaphic characteristics, disturbance, and herbivory (Myers-Smith et al. 2011, Naito and Cairns 2015). NGEE Arctic is well-suited to tackle this challenge, bringing to bear advances made under Q1 on landscape evolution, Q3 on plant traits, and Q5 on soil moisture and inundation within the modeling framework described in the Modeling and Scaling Strategy section. We will address Q4 through a series of four sub-questions that progress from (1) what is growing and how well models represent patterns; (2) why shrubs grow where they do; (3) how shrubs influence climate; and (4) integration to set the course for Phase 3 goals of robust predictions of the coupled Arctic land-climate system.

4.1: How well do land models represent present day and trending distributions in Arctic vegetation types?

Dynamic global vegetation models have traditionally focused on the tropics to mid-latitudes where vegetation represents a large store of carbon. While some studies have modeled Arctic shrub expansion (e.g., Yu et al. 2011, Lawrence and Swenson 2011, Bonfils et al. 2012), these and field studies (e.g., Blok et al. 2011) suggest that better process representation, including nutrient limitation and new plant functional types, are needed to represent dynamic vegetation under climate change. The first step in this effort is to evaluate current model performance, for which comparing model output with observations is a robust approach. To gauge the performance of current (i.e., non-dynamic) and new dynamic vegetation models at plot to watershed scales, observational data and syntheses must be developed to characterize and map fine-scale vegetation distributions. New fine-scale characterization data and maps are needed

Figure 18. Three processes for increasing arctic shrub cover and biomass: (a) infill of new individuals in shrub patches; (b) emergence of shrub canopy and growth; and (c) advancing shrubline into new areas (from Myers-Smith et al. 2011). While warmer growing seasons directly enhance growth and infill, recruitment to new areas depends on the interaction of biotic processes, physical-hydrological changes, and disturbance such as fire to prepare the surface for germination (Epstein et al. 2001).


that extend existing, coarser landscape- to circumpolar-scale vegetation-type maps (Walker 2005, Walker et al. 2006) and moderate-scale satellite remote sensing (Beck and Goetz 2011), and enable improved mapping of vegetation communities as well as geomorphic and climatic influences on those communities through time. In addition, evaluating model outcomes for plant types and biomass against multi-scale data sets requires appropriate protocols and metrics. We will leverage the ILAMB evaluation framework to quantify model performance and assess how new model representations affect the ability of models to capture contemporary vegetation distributions.

Task 4.1A: Observation-based maps of vegetation and vegetation properties: Maps of vegetation distributions, properties, and ecoregions will be created, along with uncertainties in the derived data products (Hoffman et al. 2013, Langford et al. in preparation). Starting with the Seward Peninsula, we will leverage remote sensing observations (e.g., WorldView2, MODIS, Hyperion, and Landsat) with historical photos, ground-based spectral data, LiDAR, and additional field ground-truthing. Opportunities to leverage future NASA ABoVE observations will be explored. We will define specifications for all remotely sensed products we produce, including spatial and temporal domains, resolution, and accuracy, based on an inventory of available data sources. Building on the Seward Peninsula case, we will create data products representing trends in land cover, vegetation status (starting with NDVI and ground-truthing for other attributes), and phenology for Alaska and the pan-Arctic domain using upscaling methods (Hoffman et al. 2013) and evaluate model results in the context of project hypotheses (e.g., Q3).

Task 4.1B: Simulate vegetation distribution. We will simulate the present-day distribution of major vegetation types, with an emphasis on shrubs and woody vegetation, using the current (i.e., baseline) and improved versions of TEM and ALM(ED) at watershed to pan-Arctic scales and ALM(ED)-PFLOTRAN at fine scales. Currently, ALM(ED) does not represent Arctic PFTs or N limitation. We will exercise the PFTs implemented in Task 4.2C and 4.3B, and N limitation capabilities (Xu et al. 2012) being developed in ALM(ED) by NGEE Tropics (see also TEM in Task 4.2D). We will extend process representations in the ALM and TEM models, perform model simulations at multiple scales, characterize these model results, and perform sensitivity analyses and UQ on model results in an iterative fashion. In connection with Q3, we will use model evaluation and UQ to identify new measurements required to reduce uncertainties throughout this task.

Task 4.1C: Establish benchmarking protocols and compare observed and simulated vegetation. We will compare TEM and ALM(ED) predictions of present and future vegetation type distributions with each other and with observational data products described in Task 4.1A above. To facilitate routine model-model and model-data comparisons, concurrent with Task 4.1B, we will develop evaluation metrics based on the observational data products and implement them into the ILAMB system to routinely assess the dynamic vegetation performance of multiple models. In particular, we will analyze bias and uncertainty in offline and coupled models relative to observations, probing where and why predictions do well or poorly. We will evaluate how inclusion of new observational constraints, vegetation representations, and other modifications affect model performance through re-evaluation based on new ILAMB metrics. Observed vegetation distributions cannot be assumed to be in steady state, so we will also evaluate transient conditions as well, for example, simulating 1850 to present using historical forcing. We will further document differences between models in offline and coupled modes and across models due to model modifications and identify structural and parametric uncertainties and quantify impacts of uncertainties on model predictions.

Deliverables (including MR2): Evaluation of model performance relative to observations of vegetation-type distributions, biomass, and phenology for the Seward Peninsula, Alaska, and pan-Arctic. Identification of geographic areas and processes of largest uncertainty, based on new metrics developed in ILAMB.


4.2: What are the dominant controls on rates of change in shrub cover? How will projected shrub distributions differ from present day patterns?

True shrub tundra occurs in the warmest bioclimatic subzone of the Arctic adjacent to the boreal forest, although shrub species occur in varying densities throughout most of the biome. While temperature is a dominant control on shrub cover, shrubs are also influenced by many other interacting factors, and incorporating all of these processes into a model is daunting. We anticipate that the most efficient approach is to instead first identify those processes and conditions that exert dominant control over changes in shrub cover, or are most likely to limit rates of shrubification. Our approach to improve model predictions of emergent shrub distribution will therefore include literature synthesis, expert opinion, integration of results from Q1 and Q5 on environmental controls, strategic field surveys and experiments, and informed model development and analysis.

Task 4.2A: Controls and Rates of Change. To better understand where shrub tundra is likely to occur at different time points in the future, and to prioritize our modeling efforts, we will synthesize literature and myriad data sets on the dominant controls on distribution and rates of change in shrub cover. We will use spatial data layers such as air and soil temperature, soil moisture, presence/absence of permafrost, active layer depth, edaphic characteristics, recent wildfire and permafrost-related disturbances and caribou migration routes - along with data layers on current shrub communities from Task 4.1A - to refine understanding of the controls and to create probability maps showing where shrub tundra occurs today and likelihood of occurrence in the near future. This information will also help develop maps of where shrub tundra is unlikely to occur even with climate change; for example, shrub tundra does not occur in non-acidic tundra so this type of tundra would have a low probability of shrub invasion. In addition to creating maps showing the relative suitability of different areas of the landscape, we will document the processes that are likely to limit the rate of change of shrub expansion and infill - in other words the biotic and abiotic bottlenecks - based on a combination of (1) literature review and expert opinion, (2) analysis of data from experiments in Alaska, Scandinavia, and other Arctic regions, and (3) comparison of recent observed trends shrub migration from Task 4.1A with documented changes in temperature, precipitation, and fire over the same periods.

Task 4.2B: Recruitment and Establishment. To evaluate the mechanisms important for shrub recruitment, we will survey shrub abundance, age class, seed rain, height and biomass across key environmental gradients such as temperature and disturbance chronosequences (Myers-Smith et al. 2011). These will be coupled with trait measurements in Q3. We propose to plant shrub seedlings at and above the northern/cold limit of shrub tundra as well as at a limited number of additional sites: (1) that augment existing manipulations (e.g., snow addition and removal experiments; Bokhorst et al. 2009) or disturbance events such as fire, and (2) that allow us to test hypotheses generated by the syntheses in Task 4.1 and Task 4.2A to better understand the environmental controls on shrub establishment. We will study temperature limitations by transplanting different species into passively warmed ITEX chambers, pending permits. We will evaluate how the Arctic-PFT-enabled models at the plot scale reproduce the responses in the experimental manipulations (e.g., TEM, Clein et al. 2000). This is one form of model verification and demonstrates how simulations into the future can illuminate longer-term implications of these experiments.

Task 4.2C: Implement dynamic vegetation components based on ED into models at different scales. ED is a cohort model of vegetation competition and coexistence with a representation of successional stages, and the competition for light between height-structured cohorts of various plant functional types (Moorcroft et al. 2001). The global scale ALM(ED) will be used as a platform for simulating at the pan-Arctic scale, building on progress by ACME and/or NGEE Tropics. We will evaluate Arctic-specific PFTs developed in Phase 1 and in ED collaborations outside of NGEE Arctic, and modify or augment as needed. The model will be tested using results from Task 4.3, and trait parameterizations from Q3. We will focus on processes and traits that govern plant growth, allocation, and allometry, to better predict how climate-relevant properties, such as height above snow surface and plant-carbon turnover times, will


change in response to warming. We will also develop representation of shrub recruitment (as described in Task 4.2B) and dispersal dynamics within the ALM(ED) framework. We will test uncertainty of parameters in the formulation of new model PFTs and process representations, and apply model uncertainty quantification and variance decomposition (UQ/VD, e.g., Dietze et al. 2014) to prioritize additional sampling and to quantify the reduction in uncertainties that result from new observations and model representations.

Task 4.2D: Simulate vegetation dynamics under different levels of climate forcing. We will simulate vegetation dynamics under different levels of climate forcing and at different levels of spatial and process resolution, to generate best estimates of future shrub distributions and their sensitivity to model formulations. These will be offline (not coupled to climate model) simulations with ALM(ED) and TEM. We will use TEM, for which there is ongoing effort on representing competition among PFTs (e.g., evergreen and deciduous shrubs), to investigate how N, water, and light competition influences current and future shrub distributions (Euskirchen et al. 2009, 2014).

Deliverables: Synthesis of new and published experimental and observational data on dominant controls on shrub distribution and rates of change, including recruitment and mortality. Arctic-specific plant functional types implemented in a demography-enabled model, and tested against data.

4.3: How do shrubs influence climate via carbon, water, nutrient, and energy fluxes in Arctic landscapes?

Producing Arctic PFTs adequate for exploring climate feedbacks (and testing models that incorporate them) requires better empirical characterization of the climate influences, such as albedo and carbon fluxes, associated with different plant communities. Currently, representation of climate-relevant attributes, or of the trait variation associated with different species and environmental conditions, is either insufficient or missing completely (Miller and Smith 2012, Dietze et al. 2014). Moreover, moving from the scale of individual plant types to landscapes containing a mixture of shrubs and low-statured vegetation requires consideration of the interaction between vegetation, landform, competition, and other factors that control structure and functioning. At landscape scales, shrubs influence hydrology through effects on evapotranspiration, snow distribution, snowmelt, and flow characteristics (Pearson et al. 2013), and also the energy budget and thermal regime through effects on snow thickness and albedo (Sturm et al. 2001a). They also modify biogeochemical conditions (Weintraub and Schimel 2005, DeMarco et al. 2014); preliminary NGEE Arctic data from the Seward Peninsula suggesting that N-fixing alder stands alter catena N budgets. Therefore, representing climate consequences of shifts in shrub diversity and function will require linking trait observations from Q3 with the analysis of carbon, water, and energy fluxes of plant communities proposed here. Field studies integrating plant traits and landscape-scale interactions will facilitate scaling from plants to watersheds, and development of parsimonious approaches to representing 3D processes in global land models.

Task 4.3A: Plant-type Scale. We will utilize field approaches and synthesis activities to characterize carbon, water, and energy fluxes of dominant shrub species and canopy architectures, and relate functional traits (see Q3) with plant-scale fluxes. We will quantify the environmental sensitivity of these processes across gradients of, for example, topography and nutrient availability. Remote sensing datasets, portable energy pole (PEP) and/or tram measurements, flux chambers (up to 1 m tall), sap flux measurements, hydrology, and geophysics datasets will be utilized. We will characterize full-spectrum (350-2500 nm) spectral properties and combined with traits like LAI and height from Q3 to improve representation of albedo and radiative transfer within the updated model framework, a critical step to improve the modeling of surface energy balance (Alton et al. 2007, Pearson et al. 2013, Juszak et al. 2014). Finally, we will couple with fine-scale modeling as needed and integrate with results from Q3 on plant traits and carbon consequences.

Task 4.3B: Intermediate landscape scale. To improve our ability to model fluxes of carbon, water, and energy from Arctic vegetation at intermediate scales (~100s of meters), we will take an integrative


approach that enables scaling and attribution of plant-level responses to the larger landscape. We will couple transect and/or grid sampling (with measurements from Task 4.3A), an eddy covariance site, and remote sensing of shrub-containing “ecosystem types,” such as degraded permafrost areas (e.g., dwarf birch dominant) relative to low-centered polygonal areas (with minimal emergent shrubs) to quantify effects of shrub tundra and communities. With Task 4.2, we will use UQ/VD to assess uncertainties of intermediate-scale modeling of shrub functioning and attribute improvements in model skill.

Task 4.3C: Watershed scale processes. To better understanding and model watershed carbon, energy, and water balance coupled with larger-scale topographic and hydrologic controls, we will use long-term observations of plot-scale meteorology in areas of varying shrub density on the Seward Peninsula. Fine to intermediate-scale models, remote sensing and spatial sampling will be used to evaluate the importance of the spatial structure (e.g., types of micro-topographic features, slope, aspect) and density of vegetation communities for modeling watersheds. The snow-shrub-permafrost interaction is considered to be particularly important, which potentially create a positive feedback for shrub growth (Sturm et al. 2001b). Finally, we will leverage models and observations of varying spatial resolution and complexity (e.g., structural, parametric), to address within-watershed vegetation representation and the effects of shrub expansion on carbon cycling, assessed with UQ/VD. We will investigate the effect of shrub ET on watershed water budgets jointly with Q1 and Q5 and the effect of shrub presence on active layer depth and soil temperature jointly with Q1.

Deliverables (including MR7): Published, multi-scale observational benchmark data sets for evaluating the climate impact, such as carbon and energy fluxes, of Arctic PFTs and plant communities. Fine-scale simulation of effects of shrub expansion on thermal hydrology and surface energy balance, coordinated with Q5 and IM3. Development of scaling and reduced order approaches for representing within-watershed complexity in coarse-scale modeling.

4.4: What are the major uncertainties and sensitivities in vegetation-climate feedbacks in the coupled land-atmosphere system?

Although offline land model simulations are useful for evaluating potential responses of vegetation to a changing climate, characterizing the full greenhouse gas and energy balance forcing on the atmosphere--including feedbacks--requires coupled simulations. For example, we expect changes in near-surface air temperatures and precipitation associated with large-scale changes in vegetation cover (Swann et al. 2010). Therefore, under this sub-question we will quantify climate feedbacks from dynamic vegetation, including changes in shrub cover, height, and distribution, which are predicted to occur over the next century. We will design simulations that focus on the physical interactions with climate in the beginning of Phase 2, and we expect that fully coupled experiments will begin toward the end of Phase 2. We will phase our analyses by beginning with coupled CESM/ACME simulations with imposed vegetation changes to identify the most important vegetation properties relevant to feedbacks with the atmosphere, and the most sensitive locations and seasons for these feedbacks to occur. As the dynamic vegetation model becomes available over the next three years, we will transition to fully-coupled simulations with prognostic vegetation distributions. We will perform our analyses across a range of temporal (synoptic - decadal) and spatial (~10 km - pan-Arctic) scales.

Task 4.4A: We will explore land-atmosphere interactions with current vegetation distribution, potential shrubification under the historical period of record, and future climate change. Specifically, we will perform a series of multi-decadal uncoupled and coupled CESM/ACME simulations with (1) baseline vegetation cover; (2) imposed vegetation cover changes based on existing predictions (e.g., from ArcVeg (Epstein et al. 2004b), and (3) prognostic dynamic vegetation cover change based on ALM-ED simulations and from TEM plus ALFRESCO. We will also perform simulations that account for new model capabilities that affect surface energy budgets, e.g., those associated with integrating the direct foliar N control of photosynthesis (Xu et al. 2012, Ghimire et al. 2015), which leads to very different


latent and sensible heat partitioning. We will evaluate impacts on surface air temperature, precipitation, winds, and humidity, and assess statistical significance as in Subin et al. (2012) and Murphy et al. (2012).

Task 4.4B: We will identify key plant traits with critical feedback to climate. As mentioned in Task 4.2D, several of the model changes to plant traits and trade-offs that we expect to integrate in ALM and ALM-ED will affect the surface energy budget and therefore interactions with climate (e.g, precipitation, water vapor, air temperature). We will first perform offline simulations to identify dominant mechanisms relevant to atmospheric feedbacks (i.e., carbon and energy balances), and then design a few fully-coupled CESM/ACME simulations to investigate their impacts. Specific traits we will investigate are those that govern: photosynthesis and transpiration; plant morphology, growth, and height relative to snow; phenology; dispersal; nutrient acquisition; and plant-fire interactions.

Deliverables: Quantification of the impact of shrubification on future climate via biogeochemical and energy budgets, with imposed and prognostic dynamic vegetation. Identification of traits important for interactions between the land and atmosphere, and coupled simulations to quantify feedbacks with the atmosphere associated with those traits. Priorities for data needs and model development in Phase 3.

QUESTION 5. WHERE, WHEN, AND WHY WILL THE ARCTIC BECOME WETTER OR DRIER? The spatial distribution and temporal dynamics of soil saturation and inundation in Arctic landscapes drive subsurface and surface ecosystem responses, CO2 and CH4 fluxes and the local- to regional-scale energy balance (Hinzman et al. 2005, Lawrence et al. 2011, Grosse et al. 2011, Lara et al. 2015, Schuur et al. 2015, Andresen et al. 2015, Natali et al. 2015). Even relatively small climate driven changes in thermal-hydrology may drive significant shifts in ecosystem response (Swenson et al. 2012) and vice-verse (Subin et al. 2013), but current limitations in landscape specific process representation in models causes high uncertainty in predicted coupled eco-hydrologic behavior (Slater et al. 2007). Earth System Models predict an overall drying of Arctic soils by the end of the century due to increased summer ET and active layer depth (Walsh et al. 2005, Avis et al. 2011, Koven et al. 2013b). However, they are not able to represent the spatial and temporal heterogeneity in soil moisture or the increasing and decreasing lake, pond and wetland areas observed in the Arctic over recent decades (Smith et al. 2005, Hinkel et al. 2007, Wolfe et al. 2011, Karlsson et al, 2014, Chen et al. 2013, 2014). Moreover, the predicted increase in warm-season soil drying may be confounded by changes in global atmospheric moisture transport in cold months (Walsh et al. 2005) which, through deeper snow pack, may increase soil warming and snow melt, and increase spring runoff to, and impoundment in, wetland environments.

We currently do not know how these missing patterns and dynamics in wetness will influence feedback from the terrestrial Arctic to the climate system. We propose that resolving the coupled surface and subsurface eco-hydro-thermal-mechanical processes and properties that control permafrost, water storage capacity and vertical and horizontal hydrologic fluxes will enable better prediction of where and when Arctic landscapes will become wetter or drier. The science undertaken in Q5 aims to integrate data and modeling advances in Q1 through Q4 with additional new knowledge on the structure, function and evolution of hydrologic stores and pathways across diverse Arctic and sub-Arctic landscapes. The deliverable will be new understanding of model uncertainty from model inter-comparisons, and new knowledge from observations and benchmark data sets that inform the development, application and evaluation of hydrologically realistic fine and intermediate scale models. These data and models will be used to develop new process parameterizations for inclusion in the NGEE Arctic and ACME in Phase 3.

5.1 How well do regional- to global- scale models represent the spatial and temporal distribution of water across contrasting Arctic landscapes?

Our ability to predict pan-Arctic hydrology is limited by our current representations of (1) coupled surface and subsurface eco-thermal-hydrologic-deformational processes, (2) land surface and subsurface material properties that influence heat and water transport, and (3) land surface-climate interactions that distribute precipitation and radiation across the landscape (Slater et al. 2007, Swenson et al. 2012, Subin


et al. 2013). Tasks undertaken to answer this question aim to elucidate similarities and differences between land models, compare performance of models in relation to benchmark data sets and identify key improvements required to enhance prediction of the past, present and future distribution (e.g., snow, soil moisture, inundation) and fluxes (surface and subsurface lateral flow and ET) of water in the arctic landscape. We will synthesize existing and new, disparate, multi-scale, spatial and time-series data on precipitation, snow properties, surface and subsurface temperature, evapotranspiration, soil moisture, inundation and runoff into ILAMB (Luo et al. 2012) benchmark data sets and perform a land-model inter-comparison with participation from the broader modeling community.

Task 5.1A: ILAMB hydrology benchmark data. We will synthesize climate, meteorological, thermal and hydrological in-situ and remotely sensed data for contrasting, well-observed watersheds and river basins in the Arctic and sub-Arctic for model evaluation, interrogation and inter-comparison. Ongoing efforts within the Arctic observing and modeling communities (eg. GTN-P, ArcticRims, NCEP-NCAR, NASA ABOVE, CARVE, Arctic Landscape Conservation Cooperative Imiq Hydroclimate Database, NASA-SMAP, PCN, ILAMB) will be leveraged to develop scale-aware thermal-hydrology benchmark data sets to evaluate regional to global scale hydrology predictive capability. We will incorporate these data into the new ILAMB database for use by the broader model evaluation community. We will focus on developing benchmark data for the Barrow (Arctic) and Seward (sub-Arctic) peninsulas, but may include other areas with robust multi-parameter datasets that will inform the criteria for, and development of, benchmark datasets in contrasting Arctic environments in Eurasia and Canada. This task will include synthesis of existing papers, map products and data with new remote sensing analyses (Task 5.1B) as required.

Task 5.1B: Map changes in hydrology. We will quantify trends in thermal-hydrology as a function of changing climate in contrasting climatic and physiographic regions. In close collaboration with Q1 and Q4 we will co-analyze historical and emerging remote sensing products, climate and other relevant time series data to determine the timing, rates and spatial patterns of thermal-hydrologic change in a set of contrasting Arctic landscapes. In this task, we will leverage ongoing data collection and analysis activities in other projects (e.g., GTN-P, NASA ABoVE, PCN, etc.). These multi-scale spatial data will be incorporated into the ILAMB database and used in our model analysis and comparison efforts described in Task 5.1C.

Task 5.1C: Model inter-comparison of Arctic hydrology. We will synthesize existing regional to global-scale predictions of past, present and future distributions of water in landscapes and analyze bias and uncertainty in offline and coupled models relative to our benchmark data. We will interrogate models to determine the source of differences between model predictions and identify key process(es) and land representation requirements for improved model performance. This task will specifically investigate the performance of models in predicting the temporal and spatial distribution of soil moisture, inundation and runoff at multiple scales in selected river basins across the Arctic, including the Barrow and Seward Peninsulas in relation to our hydrologic benchmark data sets. In collaboration with Q1 and Q4 tasks, we will investigate model performance in relation to coupled interactions between precipitation, snow processes, ALT, soil moisture, vegetation, thermokarst and variation in landscape properties. New model evaluation metrics will be developed within the ILAMB framework for routine assessment of model performance.


Deliverables: New scale-aware ILAMB hydrology benchmark data sets for diverse arctic landscapes, applied to evaluate our current ability to predict inundation, soil moisture and runoff using regional to global scale eco-thermal-hydrology models. A key outcome will be synthesis papers with the broader Arctic permafrost hydrology community on new ILAMB benchmark data sets and Arctic hydrology model inter-comparison results and model improvement recommendations.

5.2 What are the primary controls on the spatial and temporal distribution of water fluxes (vertical and horizontal) and residence time of water in Arctic landscapes, and how will these change in the future?

Subquestion 5.1 focuses on identifying the key model and observation gaps that lead to uncertainty in predicting landscape wetness. To address these gaps we need new data and models that enable us to examine how climate, landscape structure and connectivity, permafrost structure, subsurface properties, geological structure, and vegetation control landscape wetness as independent and interacting variables. In particular we will focus on quantifying (1) how ALT, peat formation and ground deformation compete in the evolution of soil-water storage capacity in the active layer and its impact on landscape wetting or drying, (2) the role of shallow to deep horizontal/lateral water fluxes and pathways coupled with the presence, absence, depth and thickness of permafrost in controlling wetland and hillslope soil drainage/drying, fast subsurface lateral flow paths, subsurface water residence times, and (3) how changes in landscape structure (e.g., thermokarst, soil detachments) and disturbance (e.g., fire) impact shallow and deep flow paths and soil moisture and surface inundation under current and future climate. Each of these three focus areas will also address the cross-cutting role of changes in vegetation composition and distribution and (in particular shrubs) related ET and vegetation-snow interactions on landscape wetness.

Task 5.2A Evolution of soil water storage capacity in Barrow, AK. We will develop and test new fine, intermediate and global scale models that explore and predict how active layer deepening, ground deformation/consolidation, and ecosystem processes interact and compete in the evolution of soil-water storage capacity in ice-rich permafrost. Toward this goal we will complete the development of a high-resolution eco-thermal-hydrology Barrow benchmark dataset and use it to initialize and evaluate enhanced coupled ecosystem-deformation models at fine, intermediate and pan-Arctic scales. Surface and subsurface geophysical data collected in Phase 1 will be analyzed in concert with ongoing core characterization (e.g., ice content, organic content, porosity, thermal and hydraulic conductivity), a new tracer experiment and ongoing in-situ thermal-hydrologic observations (ERT, water levels, soil moisture and temperature, and inundation dynamics). These data will be integrated with other multi-decadal high-resolution data and analyses that investigate changes in topography, geomorphology, ground temperature, water balance, inundation and vegetation. The data will be used to initialize, calibrate and evaluate representations of new coupled ecosystem, thermal, hydrologic, deformational processes in the ATS model (Lewis et al. 2012, Painter et al. 2013, Karra et al. 2014), CLM4.5 (Lee et al. 2014) and the Alaska Thermokarst Model. These models span a spectrum of physics–to rule-based representations of hydrology, ALT development, ground deformation and ecosystem response. The models will be compared to one another and data from the historical period to determine the key factors that determine how much pore space will be available for water storage and transport in evolving surface peat and thawing permafrost layers with climate warming. We will also explore controls on the evolution of the “protective layer” that impedes deep thawing in thermokarst environments, and may reduce soil drying under warming climate in regions with ice rich permafrost.

Task 5.2B Evolution of horizontal flow pathways in Arctic landscapes. As the active layer deepens and permafrost declines throughout Arctic environments, new surface and subsurface lateral/horizontal flow pathways will develop that may significantly alter the timing, magnitude and spatial distribution of soil moisture, inundation and runoff, as well as the redistribution of dissolved carbon, nutrients and shrubs in Arctic landscapes (Walvoord and Striegl, 2007, Lyon et al. 2010, Wolfe et al. 2011, Swenson et al. 2012, Arp et al. 2012). In hilly landscapes the position of the frost table in relation to soil layers and cryogenically and tectonically fractured shallow and deeper bedrock will determine the drainage potential


and moisture status of the hillslope. Relatively fast subsurface lateral and sub-vertical flow pathways may develop that drain and dry the overlying soil as the active layer and taliks penetrate into fractured bedrock. These drainage pathways may reduce ET and transfer more infiltrated water to toeslopes and valley bottoms, possibly causing an increase in soil moisture or inundation at lower elevations. The work outlined below will focus on the development of new data sets to characterize flow pathways in hilly landscapes in continuous and discontinuous permafrost, and the development and application of models that explore how changes in the active layer, coupled with variations in permafrost configuration (presence, absence, depth and thickness) control the partitioning of horizontal and vertical water fluxes in Arctic landscapes.

Isotopic and geochemical tracers. We will use natural abundance of isotopic and geochemical tracers and judiciously applied tracers to determine vertical and horizontal surface and subsurface flow paths and fluxes of water and dissolved nutrients and carbon Arctic and sub-Arctic landscapes. We will sample and analyze the isotopic and geochemical composition of snow and rain; pore waters in the organic layer, mineral soil, shallow bedrock and deep bedrock (springs); runoff on hillslopes; stream and river flow; and ponded water in lakes, ponds and wetlands. We will identify geochemical and isotopic fingerprints to quantify the dominant sources, sinks, flow paths, transformations, and residence times of water and dissolved constituents along vertical and horizontal flow paths in the surface and subsurface. These data will also be used to inform conceptual model development and to evaluate model predictions. This task integrates with Q2 and Q4, which focus on the role of landscape features, water flow pathways, and shrubs on soil carbon and of N concentrations, cycling, and transport in hilly landscapes.

Seward thermal-hydrologic benchmark data sets. We will develop new geophysical, in-situ, core and remotely sensed thermal-hydrology data (e.g., meteorology, snow processes, water levels/pressures, soil moisture, soil/ground properties, heat flux, soil temperature, frost table depth, runoff) along NGEE Arctic intensive transects in the Seward Peninsula. We will also make use of prior and on-going observations at or near sites of interest. Observations will be used with inverse methods to estimate the distribution of soil parameters that cannot be easily measured directly in the field (e.g., hydraulic and thermal parameters). Experiments (e.g., infiltration, tracer, heat pulse) may be performed if needed to refine estimation of hydraulic and thermal parameters. These data will be used to understand the co-variation of the thermal-hydrology properties and processes with landscape features, subsurface and surface properties and gradients (see Q1 and Q4), investigate ecosystem and biogeochemical processes and co-variation along these transects, (see Q2, Q3, and Q4) and parametrize and evaluate models of hilly continuous and discontinuous permafrost systems (Tasks IM3-IM6).

Hillslope and watershed simulations. We will enhance baseline hillslope and watershed models developed in IM tasks and apply those models in a series of numerical experiments to understand how landscape structure (see Q1), subsurface properties (see Q1 and Q5), vegetation (see Q4) and climate interact to influence surface and subsurface water storage capacity, vertical and horizontal hydrologic pathways, soil moisture and inundation in Barrow and Seward landscapes. These simulations will start with synthetic hillslope and sub-watershed scale domains that represent the range of topography, bedrock configurations and fractured zones geometry, permafrost, soils, vegetation and flow pathways of interest. As data become available through NGEE Phase 2 field and synthesis activities, the simulations will be refined to represent specific NGEE transects and field sites and calibrated with NGEE benchmark datasets to test our new representations system interactions and behavior.

Task 5.2C: Disturbance impacts on hydrology. We will determine the dominant interactions and feedbacks between landscape wetness and landscape disturbance due to fire and thermokarst. Findings from Q1 on landscape structure evolution (observations and model analyses) will inform baseline and enhanced models developed in Tasks 5.2A and 5.2B to simulate the impacts of thermokarst and permafrost degradation on water storage capacity, flux pathways and landscape wetness across a range of landscape features and settings. We will also explore the impact of fire on thermal-hydrology under current and future climate conditions using data from the Kougarok bridge fire. Outcomes from these


analyses will be used to determine how the spatial and temporal “footprint” of disturbance will drive changes in the water cycle that result in important hydrologic tipping points that drive long term changes in landscape wetness.

Deliverables (including MR6): A refined theoretical framework, new high resolution data sets and process resolving simulations that demonstrate how landscape properties and eco-thermal-hydrologic processes interact to control the distribution of water in the landscape.

5.3 How do high resolution spatial and temporal variations in soil moisture, inundation and runoff, and the evolution of these patterns and processes, interact with the climate system?

A key hypothesis of NGEE Arctic is that by improving the representation of important landscape features and their controls on ecosystem processes, we can reduce uncertainty in model predictions. Preliminary work by Lara et al. (in preparation) using the TEM model (Lara et al. 2015) shows significant increase in uncertainty in predicted future plant productivity and carbon emissions with coarsening of spatial resolution and reduction in the number of landscape classes considered by the model. In this subquestion, we will explore how our improved representation of thermal-hydrology impacts the magnitude, rate and timing of feedback to the climate system. In particular, we will assess how different representations of landscape processes ranging in realism and resolution impact future prediction of the energy balance.

Task 5.3A: High-latitude hydrological feedbacks with climate. We will investigate the role current and future high-latitude terrestrial hydrology has on regional and global climate. An important component of high-latitude hydrology that is expected to change over the coming century is the fraction of the land surface that is inundated. Previous work by this team showed large-scale atmospheric responses to changes in high-latitude lake area and dynamics (Subin et al. 2012). In addition we recently developed a simplified relationship for inundated fraction in CESM (Riley et al. 2011) based on satellite observations, allowing the model to prognose future changes in inundation associated with changes in climate. For this task we will use updated estimates of inundation (e.g., Fluet-Chouinard et al. 2015) and improved understanding of controls on hydrology facilitated by analyses in Q1and Q5 to update this relationship in CLM or ALM. Offline simulations will first be performed to analyze large-scale surface energy flux changes associated with inundation dynamics across a range of scales following the technique of Lara et al. (in preparation). Coupled simulations will be performed following the approach described in Subin et al. (2012) and Murphy et al. (2012). Because estimates of inundation are quite uncertain (Melton et al. 2013), we will perform a suite of coupled simulations using several inundation products to try and bound predicted climate feedbacks. The high-latitude region will be represented with as high a resolution as practical using the unstructured grid capability in the global models. We will analyze local (i.e., within the high-latitude region affected by changing inundation) and distal atmospheric responses (e.g., temperature, winds, precipitation). Comparisons between the most modest and largest inundation change scenarios will allow us to assess the role inundation has on climate feedbacks.

Task 5.3.B: Statistical surrogate models. We will develop statistical surrogate models that can be used to scale process resolving model results at the feature to hillslope scale to watershed scales. These models will be used to efficiently evaluate model structural and parametric uncertainty in fine to intermediate scale models (Pau et al. 2014, Liu et al. 2015). The models are constructed from observations and fine- to intermediate-resolution modeling results. Scale-aware statistical representations (e.g., dynamic probability density functions) of soil moisture, thaw depth, snow depth, etc., will be developed using the Proper Orthogonal Decomposition mapping method and Gaussian process regression models (Higdon et al. 2008, Lawrence 2004). These techniques will be used to translate observations and fine- and intermediate-resolution modeling results into functional responses applicable for integration into CLM and the probabilistic landscape transition, Arctic Thermokarst Model.

Deliverables: Land unit to watershed scale thermal-hydrologic response functions and scale-aware, data-informed statistical representations of thermal-hydrology responses to varying climate forcing, disturbance regimes, and landscape configurations and properties. Efficient statistical models suitable for


studying sensitivities and uncertainties of responses to forcing and disturbance based on statistical approaches. New understanding of the role of fine scale fractional inundation coverage and dynamics on the climate system.

MANAGEMENT PLAN AND TEAM INTEGRATION P a g e | 57

6. MANAGEMENT PLAN AND TEAM INTEGRATION Background The NGEE Arctic project has involved multidisciplinary scientists, collaborating across multiple national laboratories and universities in the United States, growing and improving since 2012. The project resides within the Energy and Environmental Sciences Directorate (EESD) of Oak Ridge National Laboratory (ORNL) and is composed of a laboratory research director (LRD), a chief scientist, and science teams, each of which has a science team leader (STL) and contributing research staff and collaborators. Institutional leads (ILs) have been designated to assist the LRD in planning and tracking budgets and deliverables across the science topic areas.

Project Roles and Teams Leadership Team

The NGEE Arctic project is led by S. D. Wullschleger, who reports to J. Gulledge (Director of the Environmental Sciences Division, within EESD at ORNL). Wullschleger has established a Leadership Team to collaboratively provide the skills, insight, and integration necessary to successfully (1) execute this large multi-institution project, (2) develop proposals to further the objectives established by DOE BER, (3) address and evaluate new science questions, (4) act as the change control board during the life of the project, and (5) provide direct feedback to the project sponsor at DOE BER both at defined intervals and upon request. The NGEE Arctic Leadership Team is composed of the LRD, CS, STLs, ILs, Data Manager (DM), and Project Manager (PM).

Defined Roles

S. D. Wullschleger, as LRD, has overall responsibility for the NGEE Arctic project and serves as the single point of contact (POC) for direct communications with program managers at DOE BER. He has full authority to manage all aspects of the NGEE Arctic project with DOE approval and works closely with the CS and the STLs for updates of milestones/deliverables and financial reports. He creates the project performance vision and communicates expectations for compliance by all participants of the NGEE Arctic project members as they fulfill their assigned missions. He oversees capability and facilities development, including leadership and succession planning, national and international collaboration, and outreach. Responsibilities and authorities for all roles on the project team are listed in Table A-1 in the Appendix.

A science advisory board has been established to provide input to the NGEE Arctic project LRD through review of plans, progress, and participation in periodic team conference calls and meetings. Members have been selected from the national and international community across a wide range of disciplines, including researchers in the carbon cycle and subsurface sciences, ecosystem and climate modelers, representatives from other state and federal agencies, data management specialists, and members who possess traditional knowledge of local native communities. We will rotate off any members who as a result of their association with the project become collaborators on the NGEE Arctic project.

The NGEE Arctic project utilizes expertise at ORNL, as well as that of external collaborators at other DOE national laboratories and at universities who actively participate in the project. Figure 19 shows the organizational chart and staffing for the NGEE Arctic project. The STLs, including T. Boden, D. Graham, C. Iversen, J. Rowland, P. Thornton, C. Wilson, and M. Torn are responsible for integrating activities within and across the science teams, gathering project data, generating regular reports, meeting safety and monitoring requirements, and managing deliverables and scientific performance.

Participants are responsible for conducting the planned studies and for meeting science team deliverables; assessing, presenting, and publishing results, including uploading data to the ORNL portal for the project, and mentoring postdoctoral associates, students, and guests. A DM is available to assist with all steps of the data life cycle. The DM resides at ORNL and leads the data management team (DMT) in coordination


with Data Representatives at each of the partner institutions. Along with the upload of data, participants request a DOI to be assigned to their data as it matures for publication. Including the DOI in all publications related to the dataset helps to fulfill a sponsor requirement for data availability and tracking.

Teams and Integration

In addition to the leadership team, participants are assigned to science teams. Key participants are named and included in the following teams, with time allocations defined in Sections 12 and 13, “Budget” and “Budget Justification.” Each team is committed to meet the deliverables listed within the Integrated Research Tasks section of the proposal. Named researchers will be assisted by post-doctoral researchers at all institutions.

Modeling and Scale Integration Team: P. Thornton (ORNL) will lead this team, with primary responsibility for coordinating modeling efforts across scales and across process domains, as well as overall coordination between the modeling team and the other science teams. E. Coon (LANL), D. Harp (LANL), and S. Kara (LANL) will develop, apply and test new fine- to intermediate-scale eco-thermal-hydrology and landscape evolution models. C. Xu (LANL) will develop, apply and test new Arctic plant ecosystem demography models. C. Wilson (LANL), C. Xu (LANL) and J. Rowland (LANL) will participate in the design, implementation and analysis of regional to global model inter-comparisons related to shrub expansion and the wetting and drying of the Arctic. D. Nicolsky (UAF), an expert in permafrost modeling, will participate in the data collection and modeling activities related to permafrost distribution and properties. S. Panda (UAF), an expert in permafrost remote sensing and modeling, will participate in the data collection and modeling activities related to permafrost distribution. B. Bolton (UAF) will continue to develop the Alaska Thermokarst Model (ATM) and work with W. Riley (LBNL) to integrate the landscape transition concepts of the ATM into the ACME Land Model (ALM). B. Busey (UAF) will continue to work with the fine-scale landscape evolution model ERODE, evaluating mechanical and thermal degradation processes in a polygonal tundra environment. W. Riley (LBNL) will work on development, testing, and application of models for high-latitude carbon and nutrient biogeochemistry, permafrost and geomorphological dynamics, ecosystem dynamics (e.g., shrubification), and hydrology. C. Koven (LBNL) will work on climate-scale model development and analysis of coupled plant and soil biogeochemistry and ecosystem demographics (e.g., shrubification). N. Bouskill (LBNL) will work on explicit microbial models, their integration with climate-scale models, and analyses of nutrient impacts on carbon-climate interactions. J. Kumar (ORNL) and F. Hoffman (ORNL) will develop landscape characterizations as model inputs at multiple scales. G. Tang (ORNL) will work on integration of biogeochemical processes and thermal hydrology processes in fine, intermediate, and climate-scale models. S. Painter (ORNL) will lead the multi-institutional fine-scale modeling efforts, including permafrost modeling and integration of existing models in a unified fine-scale modeling framework.

Figure 19. NGEE Arctic organizational structure.


Q1. Landscape Heterogeneity Team: J. Rowland (LANL), who has extensive experience in land surface dynamics and hydrology, will lead this team. S. Hubbard (LBNL), an expert in subsurface characterization and hydrogeological investigations, will lead the geophysical investigations conducted by B. Daffon (LBNL), J. Ajo-Franklin (LBNL), and J. Peterson (LBNL). Laboratory characterization of physical properties of cores will conducted by T. Kneafsey (LBNL). H. Wainwright (LBNL) will conduct data integration, and work on scaling and landscape characterization. C. Wilson (LANL), expert in hydrology and landsurface dynamics, will assist in the geomorphological characterizations and integration with hydrological investigations. E. Coon (LANL), S. Karra (LANL) and S. Painter (ORNL), experts in fine-scale modeling, will parameterize and lead simulation of coupled geomorphic and hydro-thermal responses to climate change. V. Romanovsky (UAF), an expert in permafrost monitoring and modeling, will lead the data collection and modeling activities related to permafrost distribution. W. Cable (UAF), with extensive expertise in field data collection and analysis, will lead field efforts to measure land surface and subsurface temperatures in support of permafrost characterization. D. Nicolsky (UAF), an expert in permafrost modeling, will participate in the data collection and modeling activities related to permafrost distribution. S. Panda (UAF), an expert in permafrost remote sensing and modeling, will participate in the data collection and modeling activities related to permafrost distribution. B. Bolton (UAF), an expert in permafrost hydrology and landscape change, will conduct predictive modeling related to future landsurface changes. B. Busey (UAF), with extensive expertise in field data collection and analysis, will lead field efforts to measure landsurface and subsurface temperatures in support of permafrost characterization. J. Cherry (UAF), an expert in the collection and analysis of airborne remote sensing datasets, will aid in the synthesis of historical datasets and the collection of new airborne data. J. Kumar (ORNL), an expert in modeling and data assimilation, will perform landscape characterization to inform model development, develop parameterizations and scaling strategies, and conduct simulations at fine to climate scale. F. Hoffman (ORNL) will lead the integration of the datasets into a model evaluation and benchmarking framework and perform representativeness analyses and scaling studies.

Q2. Biogeochemistry Team: D. Graham (ORNL) is an expert in microbial biochemistry and methanogenesis; he will lead this team. B. Gu (ORNL) is an expert in SOM and its interactions; he will lead tasks to measure rates and mechanisms of soil carbon transformation and carbon-mineral interactions. S. Painter (ORNL) will assist with the interpretation and modeling of soil water measurements and thaw experiments. M. Torn (LBNL) is an expert in the controls of soil organic matter dynamics and GHG flux measurements and isotopic analysis, and N. Taş (LBNL) is expert in microbial community analysis; they will lead in situ manipulation experiments and surface gas flux analysis. W. Riley and C. Koven (LBNL) will coordinate closely with the team on biogeochemical process modeling. B. Newman and J. Heikoop (LANL) are experts in isotopic geochemistry; they will contribute to the geochemical characterization of soil water and permafrost dissolved organic and inorganic matter. Postdoctoral research associates and technical staff at ORNL, LBNL and LANL will assist with each of these tasks.

Q3. Plant Traits Team: C. Iversen (ORNL) will lead this team effort. Iversen is an expert in ecosystem ecology, with a specific focus on root-soil interactions. She will coordinate closely with A. Rogers (BNL), an expert in plant physiology, on tasks associated with characterizing the variation in key above- and belowground plant functional traits in Arctic tundra. Ecologists R. Norby (ORNL) and A. Breen (UAF) will assist with field-based trait characterization. Modelers C. Xu (LLNL), B. Riley (LBNL), and P. Thornton (ORNL) will coordinate closely with the team of empiricists to develop fine- and large-scale trait-enabled models that incorporate the variation in key Arctic plant functional traits. S. Serbin (BNL), an expert in trait-spectra relationships and uncertainty analyses, will coordinate closely with Iversen and Rogers to scale and iteratively guide trait measurements. Postdoctoral research associates and technical staff at ORNL, BNL, and UAF will assist with each of these tasks.

Q4. Vegetation Dynamics Team: M. Torn (LBNL), who is a principal investigator (PI) of the ARM Carbon Project that includes three eddy covariance systems, will lead this team and many of the tasks.


She will be assisted by W. Riley (LBNL), C. Xu (LANL), E. Coon (LANL) and S. Painter (ORNL) on modeling, S. Serbin (BNL) on remote sensing and uncertainty, F. Hoffman (ORNL) on benchmarking, A. Breen (UAF) on dynamic vegetation, and Jessie Young (USGS), Brent Newman (LANL), and Jeff Heikoop (LANL) on hydrology-related field work.

Q5. Watershed Hydrology Team: C. Wilson (LANL) will lead this effort. Wilson is a hydrologist with extensive experience in coupling model development and application with field observations and experiments. She will work closely with R. Bolton (UAF), P. Thornton (ORNL), and S. Hubbard (LBNL) to lead model development, field observation and synthesis activities aimed at characterizing and predicting thermal-hydrologic properties and processes at NGEE field sites in the Barrow and Seward peninsulas. Hydrology geophysical measurements and analyses will be undertaken by B. Dafflon (LBNL) and H. Wainright (LBNL). Meteorological and runoff stations will be developed and maintained by R. Busey (UAF). In-situ water levels; snow depth and ablation surveys; tracer experiments; and piezometric, soil moisture, water chemistry, and core data will be developed by C. Wilson (LANL), J. Rowland (LANL), B. Newman (LANL), J. Heikoop (LANL), R. Bolton (UAF), V. Romanovsky (UAF), J. Peterson (LBNL) and A. Kholodov (UAF). Data and model synthesis activities will be undertaken by C. Wilson (LANL), P. Thornton (ORNL), D. McGuire (UAF), W. Riley (LBNL), R. Bolton (UAF), J. Kumar (ORNL) and J. Cherry (UAF). Enhanced fine- and intermediate-scale process and statistical models will be developed and applied by E. Coon (LANL), S. Karra (LANL), D. Harp (LANL), S. Painter (ORNL), J. Kumar (ORNL), W. Riley (LBNL), G. Bisht (LBNL), and R. Bolton (UAF).

Data Management Team: The NGEE Arctic data management team will be led by T. Boden. He will be responsible for managing the data team, interacting with the NGEE Arctic science team, and gathering input and feedback from end users in addition to oversight of the design and improvement of the data system architecture including data interoperability and systems operations. They will be responsible for the metadata management, NGEE data portal, field and laboratory data management, and web services components of the plan. Additional members of the team include L. Hook, R. Devarakonda, T. Killeffer at ORNL, and data representatives at each partner institution; B. Busey (UAF), A. Cialella (BNL), L. Hook (ORNL), E. Miller (LANL), and J. Peterson (LBNL).

Measures of Performance The NGEE Arctic team is committed to tracking and documenting performance related to all aspects of our integrated model-experiment project. As such, we have identified a number of areas for which we track quantifiable measures of performance.

Deliverables, and outcomes: Each question has associated with it multiple sub-questions, deliverables, and expected outcomes. To achieve their deliverables, each science team has listed Experiments, Observations, Modeling, and other Investigations (EOMIs) that are thought to be necessary. We will use a new project management module on our website to provide real-time access to this work breakdown structure (WBS) to participants and BER program managers. Ultimately, the module will be track deliverables, costs, and achievements of the project. As always, our goal is the timely delivery of tasks and accomplishments within budget.

Scientific productivity: A research project is often defined by publications, abstracts, posters, presentations, and conferences attended. In addition to those normal measures we add the datasets that we make available for public use and the tools that we release in the form of climate models. We will track and report the statistics in these categories.

Modeling framework: In addition to deliverables, outcomes, and scientific productivity metrics described above for the entire project, important measures of performance for the modeling effort include release of operational, verified, and evaluated code to the broader scientific community, and demonstration of model functional improvements relative to baseline performance using well-defined


benchmarking metrics. Our modeling team works under a code sharing policy adopted by consensus in Phase 1, which states (in part): “... our goal as a group is to make developments available to the larger community as soon as possible. Therefore, we will attempt to release codes to the public on submission of the primary paper describing the particular model version.” We will generate an annual modeling performance metric quantifying the number of model versions actually released compared to the number potentially releasable under our own policy. Another useful performance metric is the demonstration of improvement in model skill against a common set of benchmarks. As described in Section 5.A, we will make regular updates to our benchmarking database, and will provide an annual review of model benchmarking performance. Data management infrastructure: It is critical that as we develop scientific understanding of Arctic ecosystems, both through process studies and models, we make that knowledge available to the larger scientific community. The NGEE Arctic project is doing that through a data portal, where information generated through our analyses will be accessible in a user-friendly environment.

Leadership: While we will be careful to focus on the tasks at hand, we will also provide where appropriate scientific leadership through involvement in state and federal agency activities that will benefit from input from our multidisciplinary team of investigators. We will explore international collaborations and/or involvement in activities that will strengthen our ultimate goal of understanding carbon cycle processes across the pan-Arctic.

Safety: Given the remote setting of the NGEE Arctic project, an important measure of performance will be scientific accomplishments in the field and the laboratory supported by a sound safety plan and strong safety record. We developed a safety plan for NGEE Arctic partners and collaborators that is implemented through two manuals and videos delivered via our required safety training website. Reading and viewing of the manuals and videos is mandatory for all participants prior to embarking on a trip for field work. A safety meeting is held every day that participants will work in the field, we hold people accountable for attention to safety procedures.

Facilitating Project Integration The NGEE Arctic project has a matrixed organizational structure that was designed specifically to facilitate integration across: partner institutions, disciplines, models and experiments. This organization is strengthened by the fact that many of our tasks contribute directly to models by providing datasets for model parameterization, process representation, initialization, or evaluation. In turn, many of the modeling tasks depend on experiments and observations to provide input. Thus, there is an inherent mechanism built into our approach that requires and fosters integration. Collaboration and exchange of information within the project is achieved by members working together on experiments, observations, and synthesis of datasets that strategically inform model process representation and parameterization, and enhance the knowledge base required for model initialization, calibration, and evaluation. This concept of model-experiment integration (ModEx) requires strong collaboration between scientists developing and testing models, and those conducting research in the field and laboratory.Our planning honors this philosophy as evidenced by science milestones and outcomes that target model needs and requirements and, in turn, as reflected in a research project that both informs models and provides opportunities for scientific discoveries related to the Arctic ecosystem.

Collaboration with other Projects

The NGEE Arctic project is aware that many other teams are working toward objectives in the climate change arena. Collaboration among these teams may have significant value to advancing science that can only be discovered if there is a collegial and collaborative relationship among the key participants. We will begin by connecting the project management of NGEE Arctic and Tropics to take advantage of lessons learned and begin the quest for sharing and collaboration across the team. NGEE Arctic will leverage existing relationships and cross-project assignments to expand collaboration opportunities


initially and broaden those communication channels over time by identifying and exploiting sharing and collaboration opportunities. Our planned investigations include extensive collaboration with DOE BER projects including ACME, IDEAS, ILAMB, and their world-class user facilities EMSL, JGI, and ARM. We anticipate continued collaboration with other federally-funded research projects like the NSF-sponsored NEON project and NASA-sponsored campaigns including ABoVE and CARVE. More specific information regarding our use of DOE facilities and capabilities is available in Section 9. Our interactions with other agencies are described in Section 10, which includes letters of collaboration for specific projects.

Project Quality Assurance, Risk Management and Change Control

The NGEE Arctic project has been planned to include methods for ensuring quality in research and for implementing standard procedures for regulatory requirements. Leadership of the project has been established that provides communication among the teams via the project core team. The leadership team of this project is committed to the delivery to our sponsor of a process-rich ecosystem model based on the studies and observations of the evolution of Arctic ecosystems in a changing climate.

The project will leverage numerous existing systems and will be executed with the collaborative efforts of highly qualified researchers. The provision of adequate infrastructure and work environment has been planned in the field and at the participating institutions. Responsibility and budget authority are planned as noted in Section 6, “Management Plan and Team Integration,” and in Table A-1 in the Appendix. The collection of data and samples has been planned to ensure the long term viability where appropriate.

A framework for identifying, monitoring, and managing the risk associated with uncertainties will be established to provide tools to science leaders and the project director to ensure that risk that threatens the success of the project are mitigated in a timely and efficient manner.

We will manage our WBS using Agile project management methodologies with a Kanban board concept. Our change control process for the WBS, called branching, allows the STL to prioritize, modify, prune, or graft any future EOMI at periodic reviews triggered by the completion of an EOMI in their team. The LRD and STLs will evaluate scientific progress and accomplishments against the planned work on a routine basis. It is fully expected that, as a result of discovery, the work plans must change including the periodic opportunity for adding new studies, techniques, and collaborators. The NGEE Arctic project has implemented a change control process for handling such decisions (see the Appendix). The Core Team will continually assess and implement changes needed for the success of NGEE Arctic goals.

Project Leadership Team Reporting • Quarterly reporting including metrics for data, productivity, and project management metrics. • Monthly teleconference with BER sponsor and Leadership Team • Monthly Dashboard reporting progress and issues and other programmatic milestones to BER sponsor • Annual Report and briefing to BER sponsor • Monthly Science Talk • Annual All-Hands Meeting

INFORMATION/DATA MANAGEMENT AND COMMUNITY OUTREACH P a g e | 63

7. INFORMATION/DATA MANAGEMENT AND COMMUNITY OUTREACH Background During Phase 1, the DMT has focused on producing data guidance, identifying the data being collected, deploying tools to capture metadata and facilitate data submission, educating participants on the use of these tools, creating and maintaining the website, developing scripts to harvest continuous data and ingesting these continuous data into a relational database, generating visualization capabilities for continuous time series, assigning Digital Object Identifiers (DOIs) to data submissions with provider approval, tracking data usage, and planning for Phase 2. In Phase 2, the DMT plans to improve the holistic approach to managing data and information throughout the entire data lifecycle by improving upon metadata capture, data submission, and data availability with better outreach and training through the central DMT and the new role of Data Representatives.

For more information on the types and content of data the project will generate; the data formats; sharing; accessing; archiving; privacy policies; and intellectual property rights please refer to the Data Management Plan document in the Appendix. High-level roles and responsibilities for data management are defined and described in the Management Section of the proposal. A process flow diagram for the project data lifecycle from task planning through publication is included in the Appendix with the Data Management Plan.

Improvements to Data Management Implementation and Framework Metadata capture

Metadata is captured in the Online Metadata Editor (OME) created in Phase 1 and materials provided by NGEE investigators. Improvements are planned for Phase 2 in the development and implementation of a metadata database to enable better data search and access through the metadata records. For more information about the OME structure and standards incorporated in the tool, please refer to the Data Management Plan in the Appendix.

The NGEE Arctic portal (https://ngee-artic.ornl.gov) will continue to improve upon and provide access to the current data sharing policies (i.e., team sharing policies and a fair-use policy), data submission guidance, and data citation recommendations. Communication to the participants about these items will primarily come from the DMT and through the new Data Representatives serving at each institution. The Data Representative assists local team members to apply standards and formatting to the data throughout development while working in collaboration with other Data Representatives and the central DMT to present more consistent datasets across the project.

Visualization Planning Tool

The Seward Peninsula Site Key is a beta version visualization planning tool (projected in Google Earth; http://ngee.ornl.gov/viz/sites) to assist in the selection of site locations for the Phase 2 major field campaigns. The tool will provide images and information about potential research sites on the Seward Peninsula leading to more informed decision-making for site selections. This tool will be evaluated for its usefulness and growth forward potentially having a description of the site, listing the types of measurements being taken at the site, and links to the associated data.

Data Submission and Sharing

In Phase 2, to improve communication and implementation of data quality assurance across the project, a Data Representative will be assigned at each major participating institution to serve as a liaison between researchers at the individual institutions and the central DMT. This representative is part of the DMT and will serve as local data support notifying the central DMT of new datasets and synthesis activities; knowledgeable of general metadata standards and guidance in addition to the and DOE SC and BER “Data Access Plan” and NGEE Arctic; consults on dataset submission preparation; provides assistance

https://ngee-artic.ornl.gov/

http://ngee.ornl.gov/viz/sites


and training on NGEE Arctic tools; attends regular DMT meetings; and provides feedback to the DMT of any data related issues, problems and needs.

Data are uploaded using the data submission feature in the Online Metadata Editor (or directly to the secure NGEE Arctic FTP server when data files are large in size or number). A metadata record is required with the data submission. Within the project, data may be shared in participant-provided formats to promote collaboration across the project, awareness of others research, planning for synthesis products, and use by the modeling teams for parametrization and initialization. Data will receive quality levels as defined in quality assurance checks and assigned by STLs in consultation with the DMT. Public data sharing requires consistent data file formats, more complete documentation, and is traceable using a DOI applied to the dataset.

Data Availability

Data are discoverable through the NGEE Arctic website and NGEE Arctic Search Tool - a tool used by numerous data centers and projects developed using various open-source technologies and providing a distributed metadata harvesting, indexing, and search system. All metadata records are publicly available. Associated datasets and documentation may have access restrictions to project members only until released by the science team to the public. The NGEE Arctic project supports the sharing of data early and often to promote vital scientific collaboration within the project.

Timelines for data submission will vary depending on data type, measurements, analyses, etc. The STLs will define the submission schedules. Data Representatives will monitor these dates at their local institutions following up with STLs when necessary to check on progress and request record updates as needed. Ideal timelines including quality assurance requirements are provided in the Data Management Plan (see Appendix).

Collaborative development of the Project Management Module and the Metadata Database will lead to development of metadata records for potential/planned datasets with the estimated dates of collection and submission for internal project sharing. Metadata records will have an indicator reflecting various levels of completeness and data curation.

Model Information

The DMT will work closely with the Modeling Teams to enable searches on NGEE Arctic modeling projects and information. The DMT will work with the NGEE Arctic Leadership Team and Modeling Teams to formulate a future strategy for handling model code and output, whether a formal part of the NGEE Arctic data collection or networked elsewhere (e.g., ACME, ESGF).

Collaboration across BER Projects

Publicly accessible NGEE Arctic data are available to anyone without cost. The NGEE Arctic advanced search interface includes Arctic-relevant data holdings from CDIAC and the ARM Archive. NGEE Tropics data will be included in the interface tool as data become available. CDIAC has proposed a web service and API to interface with CDIAC’s NGEE, SPRUCE, and FACE data holdings. Quality checks created for and applied to AmeriFlux data will be applied to NGEE flux tower measurements. Meteorological gap-filling algorithms developed for AmeriFlux by CDIAC will be applied to NGEE Arctic meteorological data to produce the same suite of standardized data files as produced in AmeriFlux. Like FACE and AmeriFlux, NetCDF versions of standard products will be produced to facilitate modeling and synthesis studies. Select, model-relevant NGEE data will be published to the Earth System Grid Federation (ESGF) through the ORNL ESGF node.

Reporting

The DMT will continue to provide monthly reports to the NGEE Arctic team about the metadata records available in the search tool including new records, updated datasets, DOIs, sharing status, and dataset download information to relevant researchers. The quarterly report submission will provide information


on new metadata records for the quarter plus usage statistics including the number of data downloads, the number of unique users downloading data, and internal versus public sharing of metadata records.

The Dashboard on the NGEE Arctic website will provide high-level data summary statistics including the number of NGEE Arctic data collections available publicly, total number of data downloads, and total DOIs assigned to NGEE Arctic data products. Summaries of these statistics will also be included in quarterly reports.

Community Outreach

The NGEE Arctic website will continue to be upgraded and enhanced to improve the user experience including mobile device access. Website updates provide the public with easy access to released data and model information in addition to existing project descriptions. Participants will continue to have secured access to project restricted information with expanded collaboration capabilities as new modules are added. The DMT proposes to host an AGU Town Hall meeting at the 2015 AGU meeting. It will serve as an information session with the latest data developments and future plans within the NGEE Arctic experiment.

Data Management Deliverables • Provide data and metadata that meet the quality assurance goals of the NGEE Arctic Project and DOE

SC and BER data policies • Maintain and/or improve existing NGEE Arctic tools • Upgrade and populate the metadata database • Improve and refine NGEE Arctic data management policies and guidelines and take multiple steps to

help NGEE Arctic scientists follow these policies and guidelines (e.g., website documents to download, OME tutorials, FAQs, restrictions/defaults in the OME or data templates).

• Organize a workshop for Data Representatives to set expectations and define roles and responsibilities

• Provide a dashboard for data metrics reporting • Actively participate in future DOE BER data integration workshops • Host an AGU Town Hall Meeting . • Implement the Project Management Module • Upgrade the NGEE Arctic Web Portal to provide better access to safety training, metadata creation,

data submission, data search, and publication tracking (posters, journal articles, news articles, presentations)

KEY PERSONNEL P a g e | 67

8. KEY PERSONNEL Key personnel for NGEE Arctic Phase 2 have been selected to provide a unique combination of skills and experiences deemed necessary to achieve the project vision. Additional information about these personnel are available in Section 14, “Curriculum Vitae.”

Stan Wullschleger (ORNL) is the Project Director. He is a Distinguished R&D Scientist and Chief Scientist in the Environmental Sciences Division. He will contribute to experimental and modeling tasks in the biogeochemistry, vegetation dynamics, and hydrology and geomorphology.

Vladimir Romanovsky (UAF) is the Chief Scientist for the entire NGEE project. His research efforts will focus primarily on developing high resolution models of permafrost thermal dynamics and permafrost degradation

Peter Thornton (ORNL) will lead the modeling team. He is a Senior Research Scientist in the Environmental Sciences Division. He will serve as co-PI and science lead for the NGEE modeling activities across landscape organization and structure, biogeochemistry, plant traits, shrub dynamics, and hydrology.

Tom Boden (ORNL) is the Data Manager. He is the director of the Carbon Dioxide Information Analysis Center (CDIAC).

Kathy Huczko (ORNL) is the Project Manager. Kathy has over 30 years of experience managing projects and measuring performance. While managing scope, schedule, and cost, she will develop improved methods for planning and measuring work, improving risk management, and a dashboard reporting methodology for the project.

Science Question Leaders Joel Rowland (LANL) will lead the Question 1 Team. He will work closely with LBNL geophysics team and the ORNL landscape modeling team to characterize and understand surface and subsurface landscape properties, organization and controls at Seward field sites.

David Graham (ORNL) will lead the Question 2 Team. He is a Senior Research Scientist and Group Leader in the Biosciences Division. He will contribute his expertise in microbiology to specific tasks in the shrub dynamics tasks as well.

Colleen Iversen (ORNL) will lead the Question 3 Team to harness the variation in key Arctic plant functional traits for use in trait-enabled models. She is a Staff Scientist in the Environmental Sciences Division. She is an expert in ecosystem ecology, with a specific focus on root-soil interactions.

Margaret Torn (LBNL) will lead the Question 4 Team for Shrub-Climate feedbacks. During Phase 1, she led the “Independent Observations for Integrated Model Evaluation.” She provides expertise in terrestrial carbon and nutrient cycling and ecosystem-atmosphere trace-gas fluxes.

Cathy Wilson (LANL) will lead the Question 5 Team to explore Watershed Hydrology and work closely with the other question leaders to optimize and integrate infrastructure and observations. Wilson will work closely with the NGEE modeling team to ensure fine scale and ecosystem demography modeling efforts are aligned with the short and long term scientific goals of the NGEE project. Cathy is also the Institutional Lead for LANL.

Institutional Representatives W. Robert Bolton (UAF) will serve as the Institutional Representative for the UAF team. He will also be responsible for leading hydrologic measurements and in developing a model of surface deformation in response to melting of buried massive ice and thawing of ice-rich permafrost.

KEY PERSONNEL P a g e | 68

Susan Hubbard (LBNL) Institutional Representative for LBNL, contributing expertise in advancing the use of geophysical methods for terrestrial system characterization and monitoring and the use of integrated datasets to investigate system functioning.

Alistair Rogers (BNL) Institutional Representative for BNL is responsible for plant physiological measurements, including leaf gas exchange and biochemistry

Cathy Wilson (LANL) is serving as the LANL Institutional Representative. Cathy will also lead the Question 5 Science Team and work closely with the other question leaders to optimize and integrate infrastructure and observations. Wilson will work closely with the NGEE modeling team to ensure fine scale and ecosystem demography modeling efforts are aligned with the short and long term scientific goals of the NGEE project.

Stan Wullschleger (ORNL), Project Director is also serving as the Institutional Representative for ORNL.

USE OF DOE FACILITIES AND CAPABILITIES P a g e | 69

9. USE OF DOE FACILITIES AND CAPABILITIES IN SUPPORT OF THE NGEE ARCTIC PROJECT

Investigators working on the NGEE Arctic project made good use of DOE facilities and capabilities in executing our Phase 1 scope of research. We anticipate similar interactions with staff at these facilities in Phase 2. A summary of past, current, and future use of these resources includes the following:

Advanced Photon Source (APS), Argonne National Laboratory The APS was used in Phase 1 to investigate the role of iron or iron oxyhydroxides in soil organic matter (SOM) decomposition in arctic tundra soils. In collaboration with Matthew Newville and Antonio Lanzirotti (Beamline 13 IDE), project staff determined Fe speciation in organic and mineral soils and soil pore waters from the Barrow Environmental Observatory (BEO) using X-ray absorption spectroscopy (XAS) and micro-probe X-ray fluorescence (µXRF) spectroscopy to evaluate relative proportions of Fe(II) and Fe(III) and dominant mineralogy and organic ligands in field samples, as well as reactants and products of microbial Fe reduction in tundra soils incubated in laboratory microcosm experiments. The results of these studies will be integrated into a modeling framework to describe controls on CO2 and CH4 production in high-latitude soils.

Atmospheric Radiation Measurement (ARM): Barrow, Alaska The DOE ARM Program’s North Slope of Alaska (NSA) site maintains highly instrumented capabilities for the study of cloud, radiative, and land-atmosphere processes at high latitudes. The principal NSA facility in Barrow (established 1997) is a valuable resource for NGEE Arctic, whose researchers use ARM data streams, such as Eddy Correlation Flux Measurements (ECOR) of CO2, CH4, energy and water fluxes; soil moisture, temperature, and net radiation (AMC); and Surface Meteorological Instrumentation (MET). The ECOR mentor (Dave Cook) is co-authoring a paper with NGEE Arctic scientists as they explore the magnitude and explanation for then-observed spring burst of CH4 from polygonal landscapes on the North Slope. In addition, ARM shares its laboratory space in the Barrow Arctic Research Center (BARC) with NGEE Arctic. For land-atmosphere interactions, ARM observations of clouds and atmospheric properties—unique in the Arctic—will play a critical role in testing and parameterizing models. The project team is currently planning expanded collaborations in the multi-scale evaluation of land surface energy balance. For purposes of scaling measurements in space and time, NGEE Arctic plans to collaborate on two recent NSA deployments. First, the deployment of an ARM mobile facility (AMF3) at Oliktok Point, northwest of Prudhoe Bay and approximately 300 km from Barrow, hosts the ECOR, AMC, and MET, as well as high-precision measurements of CH4 mixing ratios. Oliktok Point is approved for unmanned aircraft that can survey ecosystem properties predicted by NGEE Arctic models. Second, in 2015 and potentially later, ARM flights between Oliktok Point and Barrow will measure CO2, CH4, and other trace gases in situ, as well as observe surface properties to test NGEE simulations.

Center for Accelerator Mass Spectrometry (CAMS), Lawrence Livermore National Laboratory The CAMS facility hosts a 10-MV FN tandem Van de Graaff accelerator for precise measurements of radiocarbon (14C), a rare isotope of carbon that is a key tool for NGEE Arctic research on soil carbon cycling. Natural abundance radiocarbon is the only way to determine whether old (previously stabilized) soil organic carbon is being decomposed, and how perturbations such as warming affect those losses. NGEE Arctic scientists work closely with CAMS scientist Karis McFarlane to prepare high-quality samples of active layer soils and permafrost for analysis.

DOE High Performance Computing (HPC) Capability In Phase 1, the project team’s NGEE Arctic modeling activities made significant use of DOE leadership computing facilities (Titan, ORNL) and mid-range HPC resources (Wolf and Mustang at LANL, and


ORNL Institutional Cluster). The team plans to continue using these resources in support of Phase 2 activities, as well as the new CADES (Computing and Data Environment for Science) capability at ORNL.

Environmental Molecular Science Laboratory (EMSL), Pacific Northwest National Laboratory The NGEE Arctic team collaborated with Nancy Hess, Errol Robinson, Stephen Callister, and Nikola Tolic to undertake ultra-high resolution mass spectrometry analysis of permafrost soil organic carbon composition and interactions from field sites in northern Alaska. EMSL facilities including electron spray ionization Fourier transform ion cyclotron resonance mass spectrometry (ESI-FTICR MS), Nano-secondary ion mass spectrometry (NanoSIMS), and 1H-13C cross-polarization magic angle spinning nuclear magnetic resonance (CP-MAS NMR) spectroscopy were used to probe the molecular signatures of soil organic matter (SOM) transformation in arctic soils. In Phase 1, we applied ESI-FTICR MS to determine SOM degradation during a simulated warming experiment. A collaborative manuscript describing results from these studies has been revised for PLoS ONE (Mann et al. 2015). For Phase 2, we expect to expand ESI-FTICR MS and other advanced spectroscopic analyses through new user proposals to assess the ecological significance of changing SOM composition due to fractionation by sorption and microbial decomposition.

Interoperable Design of Extreme Scale Application Software (IDEAS) In Phase 1, a new approach to managing complexity in our process-rich permafrost simulations was implemented in the Arcos software. The approach allowed process representations to be modularized, which enabled individual research teams to develop, refine, and evaluate against observations before deploying in a coupled simulation. The IDEAS project is further refining and extending the Arcos software. In Phase 2, we plan to use that emerging new capability in fine-scale hillslope and watershed-scale simulations and to extend our simulation of lowland tundra at the BEO to much larger scales. In addition, IDEAS is attempting to move beyond the current best practice of componentizing the mathematical capability to include componentization of scientific capability in established codes, with the goal of enhancing productivity through reuse and improved testing and benchmarking practices. In Phase 2, we plan to use that emerging capability for software interoperability in our fine-scale simulations. This will allow us to couple the best available codes for thermal hydrology with the best available models for reactive transport.

Joint Genome Institute (JGI), Walnut Creek, California A JGI community sequencing proposal (CSP) was granted during NGEE Arctic Phase 1 to study the role of microbial communities in cycling of carbon and regulation of greenhouse gas fluxes at intensive study sites on the BEO. In collaboration with Susannah Tringe and Tanya Woyke, over 120 samples were processed to determine the microbial community composition in active layer and permafrost samples (up to 2.65 m in depth) using a high-throughput molecular phylotyping technology. In total, 58 samples from active layer and permafrost layers were sequenced to determine the microbial metabolic potential (metagenomics). Additionally, 11 samples from laboratory-scale permafrost incubations were sequenced to study changes in microbial metabolic potential and activity (metatranscriptomics) in response to thaw. This CSP resulted in 1 Tbp of data, which is one of the largest collections sequencing output generated for Arctic soils. Data are archived and available at the JGI Integrated Microbial Genomes (IMG) data management system (http://img.jgi.doe.gov/). We are analyzing this massive amount of data and exploring the relationship of community composition and microbial function to biogeochemical cycles. We are also developing a CSP proposal for NGEE Arctic Phase 2 in which we propose to use metagenomics, metatranscriptomics, genome sequencing and single cell genomics capabilities to determine microbial composition, activity and physiology of permafrost microbes both in NGEE Arctic Barrow and newly developed Seward Peninsula sites in Alaska. In this CSP investigation, we aim to

http://img.jgi.doe.gov/


analyze samples from in situ and laboratory-scale warming and priming (carbon addition) experiments to determine microbial responses to increasing temperature and substrate availability.

Stanford Synchrotron Radiation Lightsource (SSRL), Stanford University The NGEE Arctic team also collaborated with John Bargar at SSRL to initiate analyses of soil organic matter interactions with iron oxyhydroxides from pore water samples collected from the BEO. This pilot study used Fe K-edge Extended X-ray Absorption Fine Structure (EXAFS) and C K-Edge Near Edge X-Ray Absorption Fine Structure (NEXAFS) spectroscopies to characterize the interactions. A manuscript describing these results has been submitted to Biogeochemistry (Herndon et al. 2015). We anticipate continuing this collaboration during Phase 2 through a user proposal to SSRL.

PROPOSED COLLABORATORS P a g e | 73

10. PROPOSED COLLABORATORS We have outlined elsewhere our use of DOE facilities and capabilities in support of the NGEE Arctic project (see Section 9, “Use of DOE Facilities and Capabilities in Support of the NGEE Arctic Project”). Interactions with staff at these facilities (listed below) either have enabled us in Phase 1 or will enable us in Phase 2 to conduct the required field and laboratory measurements and model simulations required for the project.

• Advanced Photon Source (APS), Argonne National Laboratory • Atmospheric Radiation Measurement (ARM), Barrow, AK • Center for Accelerator Mass Spectrometry (CAMS), Lawrence Livermore National Laboratory • DOE High Performance Computing (HPC) Facilities, Oak Ridge National Laboratory • Environmental Molecular Science Laboratory (EMSL), Pacific Northwest National Laboratory • Joint Genome Institute (JGI), Walnut Creek, CA • Stanford Synchrotron Radiation Lightsource (SSRL), Stanford University

In addition, we will establish Phase 2 collaborations with a number of scientists or agencies as we continue to conduct research on the North Slope and Seward Peninsula. These interactions are identified and described briefly here, along with Letters of Collaboration.

Accelerated Climate Model for Energy (ACME): In Phase 2, we will work with members of the ACME land modeling team to adopt the watershed-based representation of land surface heterogeneity. We began this effort in Phase 1 and will now expand that framework to include both implicit and explicit representation of geography for sub-grid fractional areas. We will also use the ALM-PFLOTRAN framework for coupled thermal hydrology and biogeochemistry being developed for global-scale implementation under ACME. This second topic represents an ongoing collaboration, as the initial CLM-PFLOTRAN coupling on which ALM-PFLOTRAN is based was a product of NGEE Arctic Phase 1. By the end of Phase 2, we will provide to ACME a set of improved Arctic vegetation, hydrology, and biogeochemistry parameterizations, as well as expanded benchmarking datasets for model evaluations over Arctic regions. See Letter of Collaboration from Dave Bader.

Alaska Climate Science Center (AKCSC) and the Arctic, Northwest Boreal, and Western Alaska Landscape Conservation Cooperatives (LCC): The Integrated Ecosystem Model (IEM) project is developing an ecosystem model for Alaska and Northwest Canada that is capable of forecasting how landscape structure and function might change in response to disturbance regimes, permafrost integrity, hydrology, vegetation succession and migration. In Phase 2, we will work with members of this project to incorporate schemes for land surface change or transitions into our modeling framework. This is consistent with our focus on processes related to permafrost thaw and hydrology and consequences for landscape change, including transitions in and among polygons, drained thaw lake basins, thermokarst, and other disturbance regimes. See Letters of Collaboration from A. David McGuire (AKCSC) and Zach Stevenson (LCC).

Arctic and Boreal Vulnerability Experiment (ABoVE): NASA's Terrestrial Ecology Program is in the process of planning a major field campaign which will take place in Alaska and western Canada during the next 5 to 8 years. ABoVE will seek a better understanding of the vulnerability and resilience of ecosystems and society to this changing environment. This objective is highly consistent with the goals of NGEE Arctic, and thus we anticipate numerous opportunities for collaboration, ranging from site access to data sharing to cross-project synthesis of results. We are especially keen to explore opportunities to interact with ABoVE by providing modeling expertise while at the same time receiving input and guidance on remote-sensing products that will facilitate Phase 2 and 3 milestones of NGEE Arctic. See Letter of Collaboration from Eric Kasischke

Argonne National Laboratory (ANL): The ANL Terrestrial Ecosystem Sciences (TES) Science Focus Area (SFA) on soil carbon responses to environmental change focuses on soils of the northern


circumpolar permafrost region, where huge organic carbon stocks are preserved, mostly by the cold and often wet conditions. Members from NGEE Arctic and ANL worked together during a soil sampling campaign on the Barrow Environmental Observatory (BEO). This collaboration will continue in Phase 2, especially as we establish new sites on the Seward Peninsula. We will share samples for which we can provide a suite of measurements that ultimately will be helpful as ANL interprets factors controlling spectral signatures of soil organic carbon. See Letter of Collaboration from Julie Jastrow.

Atmospheric Radiation Measurement (ARM): The DOE ARM Program’s North Slope of Alaska (NSA) site maintains highly-instrumented capabilities for the study of cloud, radiative, and land-atmosphere processes at high latitudes. As our research continues in Barrow, we will continue to benefit from the wealth of insights gained by ARM investigators over the past 20 years or more. In addition, with the deployment of the NGEE Arctic tram, we will explore opportunities for multi-scale interpretation of energy balance for permafrost-dominated land surfaces. See Letter of Collaboration from Mark Ivey.

Carbon in Arctic Reservoirs Vulnerability Experiment (CARVE): The CARVE project is currently collecting detailed measurements of important greenhouse gases on local to regional scales in the Alaskan Arctic and demonstrating new remote sensing and improved modeling capabilities to quantify Arctic carbon fluxes and carbon cycle-climate processes. Ultimately, CARVE will provide an integrated set of data that will provide unprecedented experimental insights into Arctic carbon cycling. Since flights during the CARVE mission have encompassed CO2 and CH4 concentrations on the North Slope, including the vicinity near Barrow, the NGEE Arctic project has, and will continue, to interact with NASA-sponsored researchers on multi-scale processes controlling the carbon cycle and energy balance in Alaska. In Phase 2, we will complete an analysis started in Phase 1 on the release of CH4 from tundra landscapes in spring pulses. A manuscript led by post-doc Naama Raz Yaseef (LBNL) is in preparation. We anticipate similar interactions with CARVE. See Letter of Collaboration from Charles (Chip) Miller.

International Land Model Benchmarking Project (ILAMB): The ILAMB project is designed to improve the performance of land models and, in parallel, to improve the design of new measurement campaigns to reduce uncertainties associated with key land surface processes. In Phase 2, we will collaborate with ILAMB investigators in (1) developing new model benchmarks centered on measurements proposed for collection and distribution as a part of the second phase of our project, and (2) using ILAMB diagnostic toolsets to benchmark and evaluate model processes (i.e., ModEx). See Letter of Collaboration from Forrest Hoffman.

Interoperable Design of Extreme Scale Application Software (IDEAS): The IDEAS project has as its goal to qualitatively change the culture of extreme-scale computational science and to provide a foundation that enables transformative next-generation predictive science and decision support. Two case studies are being considered in IDEAS, one of which focuses on fine-scale modeling of polygonal ground at our intensive field site in Barrow. In Phase 2, we plan to use that emerging new capability in fine-scale hillslope and watershed-scale simulations and to extend our simulation of lowland tundra to much larger scales. In addition, IDEAS is attempting to move beyond the current best practice of componentizing the mathematical capability to include componentization of scientific capability in established codes, with the goal of enhancing productivity through reuse and improved testing and benchmarking practices. We plan to use that emerging capability for software interoperability in our fine-scale simulations. See Letter of Collaboration from Lois McInnes and Michael Heroux.

National Center for Atmospheric Research (NCAR): Scientists at NCAR have a long history of incorporating knowledge about Arctic ecosystems into climate models. Because of this experience, there are ample, near-term opportunities to pursue model-inspired research between NGEE Arctic and NCAR staff. In Phase 2, Cathy Wilson (LANL) proposes to collaborate with David Lawrence to explore critical uncertainties related to permafrost thaw and surface and groundwater hydrology in the Arctic. This relates directly to deliverables outlined in Q5. See Letter of Collaboration from David Lawrence.


National Ecological Observatory Network (NEON): A major goal of NEON is to further the understanding of the impacts of climate change on continental scale ecology and to enable analyses and forecasts of ecosystem responses in biodiversity, biogeochemistry, and ecohydrology. NEON plans several field site deployments in Alaska, including one near our intensive study site outside Barrow. It would be to our advantage to link our process studies to the long-term monitoring not only at Barrow but across all their study sites in Alaska (e.g., Healy, Delta Junction, Poker Flat, and Toolik Lake). See Letter of Collaboration from Russ Lea.

Next-Generation Ecosystem Experiments (NGEE Tropics): NGEE Tropics is a new project sponsored by the DOE, BER with the goal to develop a new hierarchical, modular modeling platform that integrates crucial processes needed to represent tropical forest ecosystem responses to global changes, including belowground biogeochemistry, plant demography and ecophysiology, plant functional traits, and aquifer-to-canopy hydrology. Just launched in 2015, NGEE Tropics shares many objectives and approaches with the NGEE Arctic project. In Phase 2, we will leverage these similarities to jointly pursue topics of interest to the larger goal of improving ESMs. We anticipate close collaboration and formation of working groups across topics of plant traits, vegetation dynamics, and data management. See Letter of Collaboration from Jeff Chambers

Permafrost Carbon Network (PCN): The goal of the PCN is to synthesize and link existing research about permafrost carbon and climate in a format that can be assimilated by biospheric and climate models, and that will contribute to future assessments of the Intergovernmental Panel on Climate Change (IPCC). Because synthesis is a major component of NGEE Arctic, we will continue to interact with the PCN in activities related to permafrost thaw, carbon cycle processes, hydrology, and thermokarst. This is an extension of Phase 1 activities that yielded several join publications between the PCN and NGEE Arctic. Cathy Wilson (LANL) will be our point of contact between NGEE Arctic and the PCN. See Letter of Collaboration from Ted Schuur.

BIBLIOGRAPHY P a g e | 93

11. BIBLIOGRAPHY Aleina FC, Brovkin V, Muster S, et al. 2013. A stochastic model for the polygonal tundra based on

Poisson–Voronoi diagrams. Earth System Dynamics 4: 187–198.

Ali AA, Xu C, Rogers A, McDowell NG, Medlyn BE, Fisher RA, Wullschleger SD, Reich PB, Vrugt JA, Bauerle WL, Santiago LS, Wilson CJ. 2015a. Global scale environmental control of plant photosynthetic capacity. Ecological Applications. In press.

Ali AA, Xu C, Rogers A, Wullschleger SD, McDowell NG, Fisher RA, Massoud EC, Vrugt JA, Muss J, Fisher JB, Reich PB, Wilson CJ. 2015b. A global-scale mechanistic model of photosynthetic capacity as an outcome of optimized nitrogen allocation. Environmental Research Letters. In preparation.

Allison SD. 2006. Soil minerals and humic acids alter enzyme stability: implications for ecosystem processes. Biogeochemistry 81: 361-373.

Allison SD, Wallenstein MD, Bradford MA. 2010. Soil-carbon response to warming dependent on microbial physiology. Nature Geoscience 3: 336-340.

Allison SD. 2012. A trait-based approach for modelling microbial litter decomposition Gessner, M (ed). Ecology Letters 15: 1058–1070.

Alton P, Mercado L, North P. 2007. A sensitivity analysis of the land-surface scheme JULES conducted for three forest biomes: Biophysical parameters, model processes, and meteorological driving data. Global Biogeochemical Cycles 20: 11.

Andresen CG, Lougheed VL. 2015. Disappearing Arctic tundra ponds: Fine-scale analysis of surface hydrology in drained thaw lake basins over a 65year period (1948-2013). Journal of Geophysical Research-Biogeosciences 120: 466-479.

Arp CD, Whitman MS, Jones BM, Kemnitz R, Grosse G, Urban FE. 2012. Drainage network structure and hydrologic behavior of three lake-rich watersheds on the arctic coastal plain, Alaska. Arctic Antarctic and Alpine Research 44: 385-398.

Atchley AL, Painter SL, Harp DR, Coon ET, Wilson CJ, Liljedahl AK, Romanovsky VE. 2015. Using field observations to inform thermal hydrology models of permafrost dynamics with ATS (v0.83), Geosci. Model Dev. Discuss. 8: 3235-3292. doi:10.5194/gmdd-8-3235-2015.

Avis CA, Weaver AJ, Meissner KJ. 2011. Reduction in areal extent of high-latitude wetlands in response to permafrost thaw. Nature Geoscience 4: 444-448.

Banskota A, Serbin S, Wynne R, Thomas V, Falkowski M, Kayastha N, Gastellu-Etchegorry J, Townsend P. 2015. An LUT-based inversion of DART model to estimate forest LAI from hyperspectral data. Selected Topics in Applied Earth Observations and Remote Sensing. IEEE Journal of PP 1-14.

Bekryaev RV, Polyakov IV, Alexeev VA. 2010. Role of polar amplification in long-term surface air temperature variations and modern arctic warming. Journal of Climate 23(14): 3888-3906. doi:10.1175/2010JCLI3297.1.

Belshe EF, Schuur EAG, Grosse G. 2013. Quantification of upland thermokarst features with high resolution remote sensing. Environmental Research Letters 8: 035016.

Benning J, Yang D. 2005. Adjustment of daily precipitation data at Barrow and Nome Alaska for 1995-2001. Arctic, Antarctic, and Alpine Research 37: 276-283.

Beringer J, Lynch AH, Chapin III FS, Mack M, Bonan GB. 2001. The representation of Arctic soils in the land surface model: The importance of mosses. Journal of Climate 14: 3324-3335.


Berner LT, Beck PSA, Bunn AG, Goetz SJ. 2013. Plant response to climate change along the forest-tundra ecotone in northeastern Siberia. Global Change Biology 19: 3449–62.

Bieniek PA, Walsh JE, Thoman RL, Bhatt US. 2014. Using climate divisions to analyze variations and trends in Alaska temperature and precipitation. Journal of Climate 27: 2800–2818.

Blok D, Sass-Klassen U, Schaepman-Stub G et al. 2011. What are the main climate drivers for shrub growth in Northeastern Siberian tundra? Biogeosciences 8:1169-1179.

Blok D, Weijers S, Welker JM, Cooper EJ, Michelsen A, Löffler J, Elberling B. 2015. Deepened winter snow increases stem growth and alters stem δ 13 C and δ 15 N in evergreen dwarf shrub Cassiope tetragona in high-arctic Svalbard tundra. Environ. Res. Lett. 10 044008.

Bonan GB, Lawrence PJ, Oleson KW, et al. 2011. Improving canopy processes in the Community Land Model version 4 (CLM4) using global flux fields empirically inferred from FLUXNET data. Journal of Geophysical Research-Biogeosciences 116.

Bouskill NJ, J Tang, WJ Riley and EL Brodie. 2012. Trait-based representation of biological nitrification: model development, testing, and predicted community composition. Frontiers in Microbiology 3: Article 364. doi:10.3389/fmicb.2012.00364.

Bouskill NJ, WJ Riley and JY Tang. 2014. Meta-analysis of high-latitude nitrogen-addition and warming studies implies ecological mechanisms overlooked by land models. Biogeosciences 11: 6969-6983. doi:10.5194/bg-11-6969-2014.

Bridgham SD, Cadillo-Quiroz H, Keller JK, Zhuang Q. 2013. Methane emissions from wetlands: biogeochemical, microbial, and modeling perspectives from local to global scales. Global Change Biology 19: 1325-1346.

Bünemann EK. 2008. Enzyme additions as a tool to assess the potential bioavailability of organically bound nutrients. Soil Biology and Biochemistry 40: 2116-2129.

Burns RG, DeForest JL, Marxsen J, Sinsabaugh RL, Stromberger ME, Wallenstein MD, Weintraub MN, Zoppini A. 2013. Soil enzymes in a changing environment: Current knowledge and future directions. Soil Biology and Biochemistry 58: 216-234.

Cahoon SMP, Sullivan PF, Shaver GR, Welker JM, Post E, Holyoak M. 2012. Interactions among shrub cover and the soil microclimate may determine future Arctic carbon budgets. Ecology Letters 15: 1415–22. doi:10.1111/j.1461-0248.2012.01865.x.

Cariboni J, Gatelli D, Liska R, Saltelli A. 2007. The role of sensitivity analysis in ecological modelling, Ecol. Modell. 203(1-2): 167–182. doi:10.1016/j.ecolmodel.2005.10.045.

Chae Y, Kang SM, Jeong S-J, Kim B, Frierson DMW. 2015. Arctic greening can cause earlier seasonality of Arctic amplification. Geophys. Res. Lett. 42: 536–541. doi:10.1002/2014GL061841

Chapin FS, Bret-Harte MS, Hobbie SE, Zhong H. 1996. Plant functional types as predictors of transient responses of Arctic vegetation to global change. Journal of Vegetation Science 7: 347-358.

Chapin FS, Sturm M, Serreze MC, McFadden JP, Key JR, Lloyd AH, McGuire AD, Rupp TS, Lynch AH, Schimel JP, Beringer J, Chapman WL, Epstein HE, Euskirchen ES, Hinzman LD, Jia G, Ping CL, Tape KD, Thompson CDC, Walker DA, Welker JM. 2005. Role of land-surface changes in arctic summer warming. Science 310: 657–60. doi:10.1126/science.1117368.

Chen J, LeBoeuf EJ, Dai S, Gu B. 2003. Fluorescence spectroscopic studies of natural organic matter fractions. Chemosphere 50: 639-647.

http://dx.doi.org/10.3389/fmicb.2012.00364

http://dx.doi.org/10.5194/bg-11-6969-2014


Chen M, Rowland JC, Wilson CJ, Altmann GL, Brumby SP. 2013. The importance of natural variability in lake areas on the detection of permafrost degradation: a case study in the Yukon Flats, Alaska. Permafrost and Periglacial Processes 24: 224-240.

Chen M, Rowland JC, Wilson CJ, Altmann GL, Brumby SP. 2014. Temporal and spatial pattern of thermokarst lake area changes at Yukon Flats, Alaska. Hydrological Processes 28: 837-852.

Chowdhury RT, Herndon EM, Phelps TJ, Elias DA, Gu B, Liang L, Wullschleger SD, and Graham DE. 2015. Stoichiometry and temperature sensitivity of methanogenesis and CO2 production from saturated polygon tundra in Barrow, Alaska. Global Change Biology 21: 7220737.

Ciais P, Sabine C, Bala G, Bopp L, Brovkin V, Canadell J, Chhabra A, DeFries R, Galloway J, Heimann M, Jones C, Le Quéré C, Myneni RB, Piao S, Thornton P. 2013. Carbon and other biogeochemical cycles. In Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [TF Stocker, D Qin, G-K Plattner, M Tignor, SK Allen, J Boschung, A Nauels, Y Xia, V Bex, PM Midgley (eds)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA.

Clein J, Kwiatkowski B, McGuire AD, Hobbie JE, Rastetter EB, Melillo JM, Kicklighter DW. 2000. Modeling carbon responses of tundra ecosystems to historical and projected climate: a comparison of a plot- and a global-scale ecosystem model to identify process-based uncertainties. Global Change Biology 6: S127-S140.

Conant RT, Ryan MG, Ågren GI, Birge HE, Davidson EA, Eliasson PE, Evans SE, Frey SD, Giardina CP, Hopkins FM, Hyvönen R, Kirschbaum MUF, Lavallee JM, Leifeld J, Parton WJ, Megan Steinweg J, Wallenstein MD, Martin Wetterstedt JÅ, Bradford MA. 2011. Temperature and soil organic matter decomposition rates – synthesis of current knowledge and a way forward. Global Change Biology 17: 3392-3404.

Cornelissen JHC, Aerts R, Cerabolini B, Werger MJA, van der Heijden MGA. 2001. Carbon cycling traits of plant species are linked with mycorrhizal strategy. Oecologia 129: 611–619.

Cresto Aleina F, Brovkin V, Muster S, Boike J, Kutzbach L, Sachs T, Zuyev S. 2013. A stochastic model for the polygonal tundra based on Poisson–Voronoi diagrams. Earth Syst. Dynam. 4(2): 187-198. doi:10.5194/esd-4-187-2013.

Crowther TW, Maynard DS, Crowther TR, Peccia J, Smith JR, Bradford MA. 2014. Untangling the fungal niche: the trait-based approach. Frontiers in Microbiology 5.

DeMarco J, Mack MC, Bret-Harte MS. 2014. Effects of arctic shrub expansion on biophysical vs. biogeochemical drivers of litter decomposition. Ecology 95: 1861–1875. doi:10.1890/13-2221.1.

Dick RP (ed). 2011. Methods of Soil Enzymology. Soil Science Society of America, Madison, WI.

Dietze MC, Serbin SP, LeBauer DS, Davidson CD, Desai AR, Feng X, Kelly R, Kooper RDS, Mantooth J, McHenry K. 2014. A quantitative assessment of a terrestrial biosphere model's data needs across North American biomes. Journal of Geophysical Research – Biogeosciences 119(3): 286-300.

Epstein HE, Chapin FS III, Walker MD, Starfield AM. 2001. Analyzing the functional type concept in arctic plants using a dynamic vegetation model. Oikos 95: 239–252. doi:10.1034/j.1600-0706.2001.950206.x.

Epstein HE, Beringer J, Gould WA, Lloyd AH, Thompson CD, Chapin FS, Michaelson GJ, Ping CL, Rupp TS, Walker DA. 2004a. The nature of spatial transitions in the Arctic. Journal of Biogeography 31: 1917-1933.


Epstein HE, Calef MP, Walker MD, Chapin FS, Starfield AM. 2004b. Detecting changes in arctic tundra plant communities in response to warming over decadal time scales. Global Change Biology 10: WOS:000222869600008, 1325-1334.

Eugster W, Rouse WR Sr RAP, McFadden JP, Baldocchi DD, Kittel TGF, Iii FSC, Liston GE, Vidale PL. 2000. Land–atmosphere energy exchange in Arctic tundra and boreal forest: available data and feedbacks to climate. Global Change Biology 6(51): 84–115.

Euskirchen ES, Mcguire AD, Chapin FS, Yi S, Thompson CC. 2009. Changes in vegetation in northern Alaska under scenarios of climate change, 2003-2100: implications for climate feedbacks. Ecological Applications 19: 1022-1043.

Euskirchen ES, Carman TB, Mcguire AD. 2014. Changes in the structure and function of northern Alaskan ecosystems when considering variable leaf-out times across groupings of species in a dynamic vegetation model. Global Change Biology 20: 963–978. doi:10.1111/gcb.12392.

Fisher JB, Badgley G, Blyth E. 2012. Global nutrient limitation in terrestrial vegetation. Global Biochemical Cycles 26: GB3007.

Fisher JB, Sikka M, Oechel WC, et al. 2014. Carbon cycle uncertainty in the Alaskan Arctic. Biogeosciences 11: 4271–4288.

Fisher RA, Koven C, Kueppers LM, Rogers A, Iversen CM. 2015a. NGEE Tropics White Paper.

Fisher RA, Muszala S, Verteinstein M, Lawrence P, Xu C, McDowell NG, Knox RG, Koven C, Holm J, Rogers BM, Lawrence D, Bonan G. 2015b. Taking off the training wheels: the properties of a dynamic vegetation model without climate envelopes. Geosci. Model Dev. Discuss. 8: 3293-3357.

Fluet-Chouinard E, Lehner B, Rebelo L-M, Papa F, Hamilton SK. 2015. Development of a global inundation map at high spatial resolution from topographic downscaling of coarse-scale remote sensing data. Remote Sensing of Environment 158: 348-361.

Freschet GT, Cornelissen JHC, van Logtestijn RSP, Aerts R. 2010. Evidence of the ‘plant economics spectrum’ in a subarctic flora. Journal of Ecology 98: 362-373.

Friedlingstein P, Cox P, Betts R, et al. 2006. Climate-carbon cycle feedback analysis: Results from the (CMIP)-M-4 model intercomparison. Journal of Climate 19: 3337-3353.

Friedlingstein P, Meinshausen M, Arora VK, Jones CD, Anav A, Liddicoat SK, Knutti R. 2014. Uncertainties in CMIP5 climate projections due to carbon cycle feedbacks. Journal of Climate 27: 511-526.

Friend AD. 2010. Terrestrial plant production and climate change. Journal of Experimental Botany 61: 1293-1309.

Frost GV, Epstein HE, Walker DA. 2014. Regional and landscape-scale variability of Landsat-observed vegetation dynamics in northwest Siberian tundra. Environmental Research Letters 9: 025004. doi:10.1088/1748-9326/9/2/025004.

Gangodagamage C, Rowland JC, Hubbard SS, Brumby SP, Liljedahl AK, Wainwright H, Wilson CJ, Altmann GL, Dafflon B, Peterson J, Ulrich C, Tweedie CE, Wullschleger SD. 2014. Extrapolating active layer thickness measurements across Arctic polygonal terrain using LiDAR and NDVI data sets. Water Resources Research 50: 6339-6357. doi:10.1002/2013WR014283.

Gardner WP, Hammond GE, Lichtner PC. 2015. High performance simulation of environmental tracers in heterogeneous domains. Groundwater 53: 71-80.

http://dx.doi.org/10.1002/2013WR014283

http://dx.doi.org/10.1002/2013WR014283


Ghimire B, Riley WJ, Koven CD, Kattge J, Rogers A, Reich PB, Wright IJ. 2015. Global leaf nitrogen functional allocation: a synthesis from the global plant traits (TRY) database. New Phytologist. In review.

Gibbard S, Caldeira K, Bala G, Phillips TJ, Wickett M. 2005. Climate effects of global land cover change. Geophys. Res. Lett. 32: L23705. doi:10.1029/2005GL024550.

Grime JP. 1974. Vegetation classification by reference to strategies. Nature 250: 26-31.

Grosse G, Harden J, Turetsky M, McGuire AD, Camill P, Tarnocai C, Frolking S, Schuur EA, Jorgenson T, Marchenko S. 2011. Vulnerability of high‐latitude soil organic carbon in North America to disturbance. Journal of Geophysical Research: Biogeosciences (2005–2012) 116: G00K06.

Gu B, Schmitt J, Chen Z, Liang L, McCarthy JF. 1995. Adsorption and desorption of different organic matter fractions on iron oxide. Geochimica et Cosmochimica Acta 59: 219-229.

Gulledge J, Schimel JP. 1998. Moisture control over atmospheric CH4 consumption and CO2 production in diverse Alaskan soils. Soil Biology and Biochemistry 30: 1127-1132.

Hartley IP, Garnett MH, Sommerkorn M, Hopkins DW, Fletcher BJ, Sloan VL, Phoenix GK, Wookey PA. 2012. A potential loss of carbon associated with greater plant growth in the European Arctic. Nature Climate Change 2: 875-879.

Hayes DJ, Kicklighter DW, McGuire AD, Chen M, Zhuang Q, Yuan F, Melillo JM, Wullschleger SD. 2014. The impacts of recent permafrost thaw on land-atmosphere greenhouse gas exchange. Environmental Research Letters 9: Article 045005. doi:10.1088/1748-9326/9/4/045005.

Herndon EM, Mann BF, Chowdhury TR, Wullschleger SD, Graham DE, Liang L, Gu B. 2015a. Rates and pathways of anaerobic organic matter decomposition in warming tundra soils from Barrow, Alaska. Soil Biology and Biochemistry (submitted).

Herndon EM, Yang Z, Bargar J, Janot N, Regier TZ, Mann BF, Graham D, Wullschleger SD, Gu B, Liang L. 2015b. Geochemical drivers of organic matter decomposition in the active layer of Arctic tundra. JGR Biogeosci. (in review).

Higdon D, Gattiker J, Williams B, Rightley M. 2008. Computer model calibration using high-dimensional output. Journal of the American Statistical Association 103: 570-583.

Hiltbrunner E, Aerts R, Bühlmann T, Huss-Danell K, Magnusson B, Myrold DD, Reed SC, Sigurdsson BD, Körner C. 2014. Ecological consequences of the expansion of N2-fixing plants in cold biomes. Oecologia 176(1): 11-24.

Hinkel KM, Jones BM, Eisner WR, Cuomo CJ, Beck RA, Frohn R. 2007. Methods to assess natural and anthropogenic thaw lake drainage on the western Arctic coastal plain of northern Alaska. Journal of Geophysical Research-Part F-Earth Surfaces 112: 1-9.

Hinzman LD, Kane DL, Gieck RE, Everett KR. 1991. Hydrologic and thermal properties of the active layer in the Alaskan Arctic. Cold Regions Science and Technology 19: 95-110.

Hinzman LD, Bettez ND, Bolton WR, et al. 2005. Evidence and implications of recent climate change in northern Alaska and other arctic regions. Climatic Change 72: 251-298.

Hinzman LD, Deal CJ, McGuire AD, Mernild SH, Polyakov IV, Walsh JE. 2013. Trajectory of the Arctic as an integrated system. Ecological Applications 23: 1837-1868. doi:10.1890/11-1498.1.

Hobara S, Koba K, Ae N, Giblin AE, Kushida K, Shaver GR. 2013. Geochemical influences on solubility of soil organic carbon in arctic tundra ecosystems. Soil Science Society of America Journal 77: 473-481.

http://dx.doi.org/10.1088/1748-9326/9/4/045005

http://dx.doi.org/10.1088/1748-9326/9/4/045005

http://dx.doi.org/10.1890/11-1498.1

http://dx.doi.org/10.1890/11-1498.1


Hobbie SE, Schimel JP, Trumbore SE, Randerson JR. 2000. Controls over carbon storage and turnover in high-latitude soils. Global Change Biology 6: 196-210.

Hodgkins SB, Tfaily MM, McCalley CK, Logan TA, Crill PM, Salska SR, Rich VI, Chanton JP. 2014. Changes in peat chemistry associated with permafrost thaw increase greenhouse gas production, Proceedings of the National Academy of Sciences 111(16): 5819-5824.

Hoffman FM, Kumar J, Mills RT, Hargrove WW. 2013. Representativeness-based sampling network design for the State of Alaska. Landscape Ecology 28(8): 1567-1586. doi:10.1007/s10980-013-9902-0.

Hubbard SS, Gangodagamage C, Dafflon B, Wainwright H, Peterson J, Gusmeroli A, Ulrich C, Wu Y, Wilson C, Rowland J, Tweedie C, Wullschleger SD. 2013. Quantifying and relating subsurface and land-surface variability in permafrost environments using surface geophysical and LiDAR datasets. Hydrogeology 21: 149-169. doi:10.1007/s10040-012-0939-y.

Hugelius G, Strauss J, Zubrzycki S, Harden JW, Schuur EAG, Ping CL, Schirrmeister L, Grosse G, Michaelson GJ, Koven CD, O'Donnell JA, Elberling B, Mishra U, Camill P, Yu Z, Palmtag J, Kuhry P. 2014. Estimated stocks of circumpolar permafrost carbon with quantified uncertainty ranges and identified data gaps. Biogeosciences 11: 6573-6593.

Iversen CM, Sloan VL, Sullivan PF, Euskirchen E, McGuire AD, Norby RJ, Walker AP, Warren JM, Wullschleger SD. 2015. The unseen iceberg: plant roots in arctic tundra. New Phytologist 205: 34-58. doi:10.1111/nph.13003.

Jansson JK, Tas N. 2014. The microbial ecology of permafrost. Nature Reviews Microbiology 12: 414-425. doi:10.1038/nrmicro3262.

Jencso KG, McGlynn BL, Gooseff MN, Wondzell SM, Bencala KE, Marshall LA. 2009. Hydrologic connectivity between landscapes and streams: transferring reach‐and plot‐scale understanding to the catchment scale. Water Resources Research 45.

Jenny H. 1941. Factors of Soil Formation: A System of Quantitative Pedology, 1st ed. McGraw-Hill, New York, London.

Jiang Y, Zhuang Q, Schaphoff S, Sitch S, Sokolov A, Kicklighter D, Melillo J. 2012. Uncertainty analysis of vegetation distribution in the northern high latitudes during the 21st century with a dynamic vegetation model. Ecology and Evolution 2: 593–614. doi:10.1002/ece3.85.

Jones BM, Grosse G, Arp CD, Jones MC, Anthony KMW, Romanovsky VE. 2011. Modern thermokarst lake dynamics in the continuous permafrost zone, northern Seward Peninsula, Alaska. Journal of Geophysical Research-Biogeosciences 116.

Jones MC, Grosse G, Jones BM, Anthony KW. 2012. Peat accumulation in drained thermokarst lake basins in continuous, ice-rich permafrost, northern Seward Peninsula, Alaska. Journal of Geophysical Research 117: G00M07.

Jorgenson MT, Osterkamp TE. 2005. Response of boreal ecosystems to varying modes. Can. J. For. Res. 35: 2100-2100.

Jorgenson MT, Shur YL, Pullman ER. 2006. Abrupt increase in permafrost degradation in Arctic Alaska. Geophysical Research Letters 33: L02503.

Jorgenson MT, Romanovsky V, Harden J, Shur Y, O'Donnell J, Schuur EA, Kanevskiy M, Marchenko S. 2010. Resilience and vulnerability of permafrost to climate change. Selected from The Dynamics of Change in Alaska's Boreal Forests: Resilience and Vulnerability in Response to Climate Warming. Canadian Journal of Forest Research 40: 1219-1236.

http://dx.doi.org/10.1007/s10040-012-0939-y

http://dx.doi.org/10.1007/s10040-012-0939-y

http://dx.doi.org/10.1111/nph.13003


http://dx.doi.org/10.1038/nrmicro3262


Juszak I, Erb AM, Maximov TC, Schaepman-Strub G. 2014. Arctic shrub effects on NDVI, summer albedo and soil shading. Remote Sens. Environ. 153: 79–89. doi:10.1016/j.rse.2014.07.021.

Kane DL, Hinzman LD, Gieck RE, McNamara JP, Youcha EK, Oatley JA. 2008. Contrasting extreme runoff events in areas of continuous permafrost, Arctic Alaska. Hydrology Research 39: 287-298.

Karlsson JM, Lyon SW, Destouni G. 2014. Temporal behavior of lake size-distribution in a thawing permafrost landscape in northwestern Siberia. Remote Sensing 6: 621-636.

Karra S, Painter SL, Lichtner PC. 2014. Three-phase numerical model for subsurface hydrology in permafrost-affected regions (PFLOTRAN-ICE v1.0). The Cryosphere Discussion 8: 1935-1950. doi:10.5194/tc-8-1935-2014.

Kattge J, Knorr W. 2007. Temperature acclimation in a biochemical model of photosynthesis: a reanalysis of data from 36 species. Plant Cell and Environment 30: 1176-1190.

Kattge J, Díaz S, et al. 2011. TRY – a global database of plant traits. Global Change Biology. doi:10.1111/j.1365-2486.2011.02451.x.

Kawahigashi M, Kaiser K, Rodionov A, Guggenberger G. 2006. Sorption of dissolved organic matter by mineral soils of the Siberian forest tundra. Global Change Biology 12: 1868-1877.

Kleber M, Mikutta R, Torn MS, Jahn R. 2005. Poorly crystalline mineral phases protect organic matter in acid subsoil horizons. European Journal of Soil Science 56: 717-725.

Koven CD, Ringeval B, Friedlingstein P, Ciais P, Cadule P, Khvorostyanov D, Krinner G, Tarnocai C. 2011. Permafrost carbon-climate feedbacks accelerate global warming. Proceedings of the National Academy of Sciences 108: 14769-14774. doi:10.1073/pnas.1103910108.

Koven CD, Riley WJ, Stern A. 2013a. Analysis of permafrost thermal dynamics and response to climate change in the CMIP5 Earth System Models. Journal of Climate 26: 1877–1900.

Koven CD, Riley WJ, Subin ZM, Tang JY, Torn MS, Collins WD, Bonan GB, Lawrence DM, Swenson SC. 2013b. The effect of vertically resolved soil biogeochemistry and alternate soil C and N models on C dynamics of CLM4. Biogeosciences 10: 7109-7131.

Koven CD, Lawrence DM, Riley WJ. 2015a. Permafrost carbon-climate feedback is sensitive to deep soil carbon decomposability but not deep soil nitrogen dynamics. Proceedings of the National Academy of Sciences of the United States of America 112(12), 3752-3757. doi:10.1073/pnas.1415123112.

Koven C, Holm J, Rogers BM, Lawrence D, Bonan G. 2015b. Taking off the training wheels: the properties of a dynamic vegetation model without climate envelopes. Geosci. Model Dev. Discuss. 8: 3293–3357.

Kraucunas I, Clarke L, Dirks J, Hathaway J, Hejazi M, Hibbard K, Huang M, Jin C, Kintner-Meyer M, van Dam K, Leung R, Li H-Y, Moss R, Peterson M, Rice J, Scott M, Thomson A, Voisin N, West T. 2015. Investigating the nexus of climate, energy, water, and land at decision-relevant scales: the Platform for Regional Integrated Modeling and Analysis (PRIMA). Climatic Change 129(3-4): 573-588. doi:10.1007/s10584-014-1064-9.

Krause S, Le Roux X, Niklaus PA, van Bodegom PM, Lennon JT, Bertilsson S, et al. 2014. Trait-based approaches for understanding microbial biodiversity and ecosystem functioning. Frontiers in Microbiology 5.

Langford ZL, Jitendra K, Hoffman FM, Sloan VL, Wullschleger SD. 2015. Mapping plant functional type distributions in Arctic ecosystems using time series WorldView-2 satellite imagery and unsupervised clustering. In preparation.

http://dx.doi.org/10.5194/tc-8-1935-2014

http://ngee-arctic.ornl.gov/biblio/author/38








http://dx.doi.org/10.1073/pnas.1103910108




Lara MJ, McGuire AD, Euskirchen ES, Tweedie CE, Hinkel KM, Skurikhin AN, Romanovsky VE, Grosse G, Bolton WR, Genet H. 2015. Polygonal tundra geomorphological change in response to warming alters future CO2 and CH4 flux on the Barrow Peninsula. Global Change Biology 21: 1634-1651. doi:10.1111/gcb.12757.

Lawrence ND. 2004. Gaussian process latent variable models for visualisation of high dimensional data. Advances in Neural Information Processing Systems 16: 329-336.

Lawrence DM, Swenson SC. 2011. Permafrost response to increasing Arctic shrub abundance depends on the relative influence of shrubs on local soil cooling versus large-scale climate warming. Environmental Research Letters 6.

Lawrence DM, Swenson SC. 2011. Permafrost response to increasing Arctic shrub abundance depends on the relative influence of shrubs on local soil cooling versus large-scale climate warming. Environmental Research Letters 6: 045504.

LeBauer DS, Wang D, Richter KT, Davidson CC, Dietze MC. 2013. Facilitating feedbacks between field measurements and ecosystem models. Ecological Monographs 83: 133-154.

Lee H, Swenson SC, Slater AG, Lawrence DM. 2014. Effects of excess ground ice on projections of permafrost in a warming climate. Environmental Research Letters 9: 124006.

Lewin KF, Ely KS, Serbin SP, Rogers A. 2015. A zero power warming chamber for investigating plant responses to rising temperature. Global Change Biology. In review.

Lewis KC, Zyvoloski GA, Travis B, Wilson C, Rowland J. 2012. Drainage subsidence associated with Arctic permafrost degradation. Journal of Geophysical Research 117: Article F04019. doi:10.1029/2011JF002284.

Li H, Wigmosta MS, Wu H, Huang M, Ke Y, Coleman AM, Leung LR. 2013. A physically based runoff routing model for land surface and earth system models. Journal of Hydrometeorology 14: 808-828.

Liljedahl AK, Hinzman LD, Schulla J. 2012. Ice-wedge polygon type controls low-gradient watershed-scale hydrology, in Tenth International Conference on Permafrost, vol. 1: International Contributions, pp. 231–236, KM Hinkel (ed), The Northern Publisher, Salekhard, Russia.

Lin et al. 2015. Optimal stomatal behavior around the world: synthesis of a global stomatal conductance database. Nature Climate Change 5: 459-464. doi:10.1038/nclimate2550.

Lindsay K, Bonan GB, Doney SC, Hoffman FM, Lawrence DM, Long MC, Mahowald NM, MooreJK, Randerson JT, Thornton PE. 2014. Preindustrial-control and twentieth-century carbon cycle experiments with the Earth System Model CESM1(BGC). Journal of Climate 27(24): 8981-9005. doi:10.1175/JCLI-D-12-00565.1.

Lipson DA, Jha M, Raab TK, Oechel WC. 2010. Reduction of iron(III) and humic substances plays a major role in anaerobic respiration in an Arctic peat soil. J. Geophys. Res. 115: G00I06.

Liu L, Schaefer K, Zhang T, Wahr J. 2012. Estimating 1992-2000 average active layer thickness on the Alaska North Slope from remotely sensed surface subsidence, J. Geophy. Res. 117: F01005.

Liu W, Huang B, Thorne PW, et al. 2015. Extended Reconstructed Sea Surface Temperature Version 4 (ERSST.v4): Part II. Parametric and structural uncertainty estimations. Journal of Climate 28: 931-951.

Luo YQ, Randerson JT, Abramowitz G, et al. 2012. A framework for benchmarking land models. Biogeosciences 9: 3857-3874.

http://dx.doi.org/10.1111/gcb.12757

http://dx.doi.org/10.1029/2011JF002284

http://dx.doi.org/10.1038/nclimate2550


Lyon SW, Morth M, Humborg C, Giesler R, Destouni G. 2010. The relationship between subsurface hydrology and dissolved carbon fluxes for a sub-arctic catchment. Hydrology and Earth System Sciences 14: 941-950.

Mack MC, Schuur EAG, Bret-Harte MS, Shaver GR, Chapin III FS. 2004. Ecosystem carbon storage in arctic tundra reduced by long-term nutrient fertilization. Nature 431: 440-443.

Maggi FM, Riley W. 2015. The effect of temperature on the rate, affinity, and 15N fractionation of NO3−

during biological denitrification in soils. Biogeochemistry. doi:10.1007/s10533-015-0095-2.

Mann BF, Herndon EM, Portier E, Tolic N, Callister SJ, Chu RK, Robinson R, Graham DE, Wullschleger SD, Liang L, Gu B. 2015. High-resolution molecular profiling of permafrost soil organic carbon composition and degradation under a warming climate. PLoS ONE. Submitted.

Marion GM, Henry GHR, Freckman DW, et al. 1997. Open-top designs for manipulating field temperature in high-latitude ecosystems. Global Change Biology 3: 20-32.

Marchenko S, Romanovsky V, Tipenko G. 2008. Numerical modeling of spatial permafrost dynamics in Alaska. Pages 1125-1130 in Proceedings of the Ninth International Conference on Permafrost, Institute of Northern Engineering, University of Alaska Fairbanks.

Mastepanov M, Sigsgaard C, Dlugokencky EJ, Houweling S, Strom L, Tamstorf MP, Christensen TR. 2008. Large tundra methane burst during onset of freezing. Nature (London) 456: 628-630.

McCalley CK, Woodcroft BJ, Hodgkins SB, et al. 2014. Methane dynamics regulated by microbial community response to permafrost thaw. Nature 514: 478-481.

McGuire AD, Sturm M, Chapin FS. 2003. Arctic transitions in the land-atmosphere system (ATLAS): Background, objectives, results, and future directions. Journal of Geophysical Research 108: 8166.

McKane RB, Johnson LC, Shaver GR, Nadelhoffer KJ, Rastetter EB, Fry B, Giblin AE, Kielland K, Kwiatkowski BL, Laundre JA, Murray G. 2002. Resource-based niches provide a basis for plant species diversity and dominance in arctic tundra. Nature 415: 68-71.

Medlyn BE, Dreyer E, Ellsworth D, et al. 2002. Temperature response of parameters of a biochemically based model of photosynthesis. II. A review of experimental data. Plant Cell and Environment 25: 1167-1179.

Medlyn BE, Duursma RA, Eamus D, Ellsworth DS, Prentice IC, Barton CVM, Crous KY, De Angelis P, Freeman M, Wingate L. 2011. Reconciling the optimal and empirical approaches to modelling stomatal conductance. Global Change Biology 17: 2134–2144.

Medvigy D, Wofsy SC, Munger JW, Hollinger DY, Moorcroft PR. 2009. Mechanistic scaling of ecosystem function and dynamics in space and time: Ecosystem Demography Model Version 2. Journal of Geophysical Research 114:G01002.

Melillo JM, Richmond TC, Yohe GW. 2014. Climate Change Impacts in the United States: The Third National Climate Assessment. U.S. Global Change Research Program. 841 pp.

Melton JR, Wania R, Hodson EL, et al. 2013. Present state of global wetland extent and wetland methane modelling: conclusions from a model inter-comparison project (WETCHIMP). Biogeosciences 10: 753-788.

Mikutta R, Kleber M, Torn M, Jahn R. 2006. Stabilization of soil organic matter: association with minerals or chemical recalcitrance? Biogeochemistry 77: 25-56.

Miller PA, Smith B. 2012. Modelling tundra vegetation response to recent arctic warming. Ambio 41: 281-291.

http://dx.doi.org/10.1007/s10533-015-0095-2


Mishra U, Riley WJ. 2012. Alaskan soil carbon stocks: spatial variability and dependence on environmental factors. Biogeosciences 9: 3637-3645.

Moorcroft PR, Hurtt GC, Pacala SW. 2001. A method for scaling vegetation dynamics: the ecosystem demography model (ED). Ecol Monogr. 71: 557-586.

Murphy LN, Riley WJ, Collins WD. 2012. Local and remote climate impacts from expansion of woody biomass for bioenergy feedstock in the southeastern United States. Journal of Climate 25: 7643-7659. WOS:000311002700019.

Myers-Smith IH, Forbes BC, Wilmking M, Hallinger M, Lantz T, Blok D, Tape KD, Macias-Fauria M, Sass-Klaassen U, Lévesque E, Boudreau S, Ropars P, Hermanutz L, Trant A, Collier LS, Weijers S, Rozema J, Rayback SA, Schmidt NM, Schaepman-Strub G, Wipf S, Rixen C, Ménard CB, Venn S, Goetz S, Andreu-Hayles L, Elmendorf S, Ravolainen V, Welker J, Grogan P, Epstein HE, Hik DS. 2011. Shrub expansion in tundra ecosystems: dynamics, impacts and research priorities. Environmental Research Letters 6: 045509. doi:10.1088/1748-9326/6/4/045509.

Myrold DD, Huss-Danell K. 2003. Alder and lupine enhance nitrogen cycling in a degraded forest soil in northern Sweden. Plant and Soil 254: 47-56.

Nadeau J, Qualls R, Nowak R, Blank R. 2007. The potential bioavailability of organic C, N, and P through enzyme hydrolysis in soils of the Mojave Desert. Biogeochemistry 82: 305-320.

Naito AT, Cairns DM. 2015. Patterns of shrub expansion in Alaskan arctic river corridors suggest phase transition. Ecology and Evolution. 5(1): 87-101. doi:10.1002/ece3.1341.

Natali SM, Schuur EaG, Webb EE, Pries CEH, Crummer KG. 2014. Permafrost degradation stimulates carbon loss from experimentally warmed tundra. Ecology 95: 602-608.

Natali SM, Schuur EAG, Mauritez M, et al. 2015. Permafrost thaw and soil moisture driving CO2 and CH4 release from upland tundra. Journal of Geophysical Research Biogeosciences 120: 525–537.

Newman BD, Throckmorton HM, Graham DE, Gu B, Hubbard SS, Liang L, Wu Y, Heikoop JM, Herndon EM, Phelps TJ, Wilson CJ, Wullschleger SD. 2015. Microtopographic and depth controls on active layer chemistry in Arctic polygonal ground. Geophysical Research Letters 42: 1808-1817. doi:10.1002/2014GL062804.

Olefeldt D, Turetsky MR, Crill PM, McGuire AD. 2013. Environmental and physical controls on northern terrestrial methane emissions across permafrost zones. Global Change Biology 19: 589-603.

Oleson, KW, Lawrence DM, Bonan GB, Drewniak B, Huang M, Koven CD, Levis S, Li F, Riley WJ, Subin ZM, Swenson SC, Thornton PE, Bozbiyik A, Fisher R, Kluzek E, Lamarque J-F, Lawrence PJ, Leung LR, Lipscomb W, Muszala S, Ricciuto DM, Sacks W, Sun Y, Tang J, Yang Z-L. 2013. Technical Description of Version 4.5 of the Community Land Model (CLM). NCAR Technical Note NCAR/TN-503+STR, National Center for Atmospheric Research, Boulder, CO, 422 pp. doi:10.5065/D6RR1W7M.

Pacific VJ, Jencso KG, McGlynn, BL. 2010. Variable flushing mechanisms and landscape structure control stream DOC export during snowmelt in a set of nested catchments. Biogeochemistry 99: 193-211.

Painter SL, S Karra. 2014. Constitutive model for unfrozen water content in subfreezing unsaturated soils. Vadose Zone Journal 13(4). doi:10.2136/vzj2013.04.0071.

Painter SL, Moulton JD, Wilson CJ. 2013. Modeling challenges for predicting hydrologic response to degrading permafrost. Hydrogeology 21: 221-224. doi:10.1007/s10040-012-0917-4.

http://dx.doi.org/10.1002/2014GL062804

http://www.cesm.ucar.edu/models/cesm1.2/clm/CLM45_Tech_Note.pdf

http://dx.doi.org/10.2136/vzj2013.04.0071

http://dx.doi.org/10.1007/s10040-012-0917-4


Panda SK, Marchenko SS, Romanovsky VE. 2014. High-resolution permafrost modeling in Denali National Park and Preserve. Page 60 in Natural Resource Technical Report, National Park Service, Department of the Interior 7.

Pappas C, Fatichi S, Rimkus S, Burlando P, Huber MO. 2015. The role of local‐scale heterogeneities in terrestrial ecosystem modeling. Journal of Geophysical Research: Biogeosciences 120: 341-360.

Pastick NJ, Jorgenson MT, Wylie BK, Rose JR, Rigge M, Walvoord MA. 2014. Spatial variability and landscape controls of near‐surface permafrost within the Alaskan Yukon River Basin. Journal of Geophysical Research: Biogeosciences 119: 1244-1265.

Pau GSH, Bisht G, Riley WJ. 2014. A reduced-order modeling approach to represent subgrid-scale hydrological dynamics for land-surface simulations: application in a polygonal tundra landscape. Geoscientific Model Development 7: 2091-2105. doi:10.5194/gmd-7-2091-2014.

Pau GSH, Shen C, Riley WJ, Liu Y. 2015. Efficient prediction of fine-scale watershed dynamics using POD Mapping Method. Hydrology and Earth System Science. Submitted.

Pearson RG, Phillips SJ, Loranty MM, Beck PSA, Damoulas T, Knight SJ, Goetz SJ. 2013. Shifts in Arctic vegetation and associated feedbacks under climate change. Nature Clim. Change 3: 673-677.

Pickett-Heaps CA, Jacob DJ, Wecht KJ, Kort EA, Wofsy SC, Diskin GS, Worthy DEJ, Kaplan JO, Bey I, Drevet J. 2011. Magnitude and seasonality of wetland methane emissions from the Hudson Bay Lowlands (Canada). Atmos. Chem. Phys. 11: 3773-3779.

Qian HF, Joseph R, Zeng N. 2010. Enhanced terrestrial carbon uptake in the northern high latitudes in the 21st century from the Coupled Carbon Cycle Climate Model Intercomparison Project model projections. Global Change Biology 16: 641-656.

Rawlins MA, Nicolsky DJ, McDonald KC, Romanovsky VE. 2013. Simulating soil freeze/thaw dynamics with an improved pan-Arctic water balance model. Journal of Advances in Modeling Earth Systems 5: 659-675.

Reich PB. 2014. The world-wide ‘fast–slow’ plant economics spectrum: a traits manifesto. Journal of Ecology 102: 275–301.

Riley WJ, Subin ZM, Lawrence DM et al. 2011. Barriers to predicting changes in global terrestrial methane fluxes: analyses using CLM4Me, a methane biogeochemistry model integrated in CESM. Biogeosciences 8: 1925-1953.

Riley WJ, Shen C. 2014. Characterizing coarse-resolution watershed soil moisture heterogeneity using fine-scale simulations and reduced-order models. Hydrology and Earth System Science 18: 2463-2483. doi:10.5194/hess-18-2463-2014.

Riley WJ, Maggi FM, Kleber M, Torn MS, Tang JY, Dwivedi D, Guerry N. 2014. Long residence times of rapidly decomposable soil organic matter: Application of a multi-phase, multi-component, and vertically-resolved model (BAMS1) to soil carbon dynamics. Geoscientific Model Development 7: 1335-1355. doi:10.5194/gmd-7-1335-2014.

Rinnan R, Rinnan Å. 2007. Application of near infrared reflectance (NIR) and fluorescence spectroscopy to analysis of microbiological and chemical properties of arctic soil. Soil Biology and Biochemistry 39: 1664-1673.

Riseborough D, Shiklomanov N, Etzelmüller B, Gruber S, Marchenko S. 2008. Recent advances in permafrost modelling. Permafrost and Periglacial Processes 19: 137-156.

http://dx.doi.org/10.5194/gmd-7-2091-2014

http://dx.doi.org/10.5194/hess-18-2463-2014




Riveros‐Iregui DA, McGlynn BL. 2009. Landscape structure control on soil CO2 efflux variability in complex terrain: scaling from point observations to watershed scale fluxes. Journal of Geophysical Research: Biogeosciences (2005–2012) 114.

Rocha AV, Loranty MM, Higuera PE, et al. 2012. The footprint of Alaskan tundra fires during the past half-century: implications for surface properties and radiative forcing. Environmental Research Letters 7: 044039.

Roche C, Dibble C, Stickel J. 2009. Laboratory-scale method for enzymatic saccharification of lignocellulosic biomass at high-solids loadings. Biotechnology for Biofuels 2: 28.

Rogers A. 2014. The use and misuse of Vc,max in earth system models. Photosynthesis Research 119: 15029.

Rogers A, Medlyn BE, Dukes JS. 2014. Improving representation of photosynthesis in earth system models. New Phytologist 204: 12-14.

Rogers A, Sloan V, Serbin SP, Kubien D, Wullschleger SD. 2015a. Earth System Models are underestimating photosynthetic capacity in the Arctic. Global Change Biology. In preparation.

Rogers A, Medlyn BE, Dukes JS, Bonan G, von Caemmerer S, Dietze MC, Kattge J, Leakey ADB, Mercado L, Niinemets U, Prentice IC, Serbin SP, Sitch S, Way DA, Zaehle S. 2015b. A roadmap for improving the representation of photosynthesis in earth system models. New Phytologist. In preparation.

Romanovsky V, Drozdov DS, Oberman NG et al. 2010. Thermal state of permafrost. Permafrost and Periglacial Processes 21: 136-155.

Rowland JC, Jones CE, Altmann G, Bryan R, Crosby BT, Hinzman LD, Kane DL, Lawrence DM, Mancino A, Marsh P, McNamara JP, Romanovsky VE, Toniolo H, Travis BJ, Trochim E, Wilson CJ, Geernaert GL. 2010. Arctic landscapes in transition: responses to thawing permafrost. EOS, Transactions American Geophysical Union 91: 229-230. doi:10.1029/2010EO260001.

Sage RF, Way DA, Kubien DS. 2008. Rubisco, Rubisco activase, and global climate change. Journal of Experimental Botany 59: 1581-1595.

Sargsyan K, Safta C, Najm HN, Debusschere BJ, Ricciuto D, Thornton P. 2014. Dimensionality reduction for complex models via Bayesian compressive sensing. International Journal for Uncertainty Quantification 4: 63-93.

Schaefer K, Lantuit H, Romanovsky VE, Schuur EAG, Witt R. 2014. The impact of the permafrost carbon feedback on global climate. Environmental Research Letters 9: 085003.

Schimel J, Schaeffer SM. 2012. Microbial control over carbon cycling in soil. Frontiers in Microbiology 3.

Schmidt MWI, Torn MS, Abiven S, Dittmar T, Guggenberger G, Janssens IA, Kleber M, Kogel-Knabner I, Lehmann J, Manning DAC, Nannipieri P, Rasse DP, Weiner S, Trumbore SE. 2011. Persistence of soil organic matter as an ecosystem property. Nature (London) 478: 49-56.

Schuur EAG, Bockheim J, Canadell JG, Euskirchen E, Field CB, Goryachkin SV, Hagemann S, Kuhry P, Lafleur PM, Lee H, Mazhitova G, Nelson FE, Rinke A, Romanovsky VE, Shiklomanov N, Tarnocai C, Venevsky S, Vogel JG, Zimov SA. 2008. Vulnerability of permafrost carbon to climate change: implications for the global carbon cycle. BioScience 58: 701-714.

Schuur EAG, Abbott BW, Bowden WB, Brovkin V, Camill P, Canadell JG, Chanton JP, Chapin FS, III, Christensen TR, Ciais P, Crosby BT, Czimczik CI, Grosse G, Harden J, Hayes DJ, Hugelius G, Jastrow JD, Jones JB, Kleinen T, Koven CD, Krinner G, Kuhry P, Lawrence DM, McGuire AD, Natali SM, O’Donnell JA, Ping CL, Riley WJ, Rinke A, Romanovsky VE, Sannel ABK, Schädel C, Schaefer K, Sky J, Subin ZM, Tarnocai C, Turetsky MR, Waldrop MP, Walter Anthony KM,









http://ngee-arctic.ornl.gov/content/arctic-landscapes-transition-responses-thawing-permafrost-0

http://dx.doi.org/10.1029/2010EO260001

http://dx.doi.org/10.1029/2010EO260001


Wickland KP, Wilson CJ, Zimov SA. 2013. Expert assessment of vulnerability of permafrost carbon to climate change. Climatic Change 119: 359-374.

Schuur EAG, McGuire AD, Schäedel C, et al. 2015. Climate change and the permafrost carbon feedback. Nature 520: 171–179.

Serbin SP, Dillaway DN, Kruger EL, Townsend PA. 2012. Leaf optical properties reflect variation in photosynthetic metabolism and its sensitivity to temperature. Journal of Experimental Botany 63: 489-502.

Serbin SP, Singh A, Mcneil BE, Kingdon CC, Townsend PA. 2014. Spectroscopic determination of leaf morphological and biochemical traits for northern temperate and boreal tree species. Ecological Applications 24(7): 1651-1669.

Serbin SP, Singh A, Desai, AR, Dubois, SG, Jablonski, AD, Kingdon CC, Kruger EL, Townsend PA. 2015. Remotely estimating photosynthetic capacity, and its response to temperature, in vegetation canopies using imaging spectroscopy. Remote Sensing of Environment. In press.

Shaftel RS, King RS, Back JA. 2012. Alder cover drives nitrogen availability in Kenai lowland headwater streams, Alaska. Biogeochemistry 107(1-3): 135-148.

Sharma S, Couturier S, Cote SD. 2009. Impacts of climate change on the seasonal distribution of migratory caribou. Global Change Biology 15: 2549-2562.

Shi X, Thornton PE, Ricciuto DM, Hanson PJ, Mao J, Sebestyen SD, Griffiths NA, Bisht G. 2015. Representing northern peatland microtopography and hydrology within the Community Land Model. Biogeosciences Discuss. 12(4): 3381-3418. doi:10.5194/bgd-12-3381-2015.

Shiklomanov NI, Streletskiy DA, Little JD, Nelson FE. 2013. Isotropic thaw subsidence in undisturbed permafrost landscapes, Geophy, Res. Letters 40: 1-6.

Shur YL, Jorgenson MT. 2007. Patterns of permafrost formation and degradation in relation to climate and ecosystems. Permafrost and Periglacial Processes 18: 7-19.

Singh A, Serbin SP, McNeil BE, Kingdon CC, Townsend PA. 2015. Imaging spectroscopy algorithms for mapping canopy foliar chemical and morphological traits and their uncertainties. Ecological Applications. In press. doi:10.1890/14-2098.1.

Slater AG, Lawrence DM. 2013. Diagnosing present and future permafrost from climate models. Journal of Climate 26: 5608–5623.

Slater AG, Bohn TJ, McCreight JL, Serreze MC, Lettenmaier DP. 2007. A multimodel simulation of pan-Arctic hydrology. Journal of Geophysical Research-Biogeosciences 112.

Slette IJ, Iversen CM, Sloan VL, Childs J, Norby RJ, Wullschleger SD. 2015. Characterizing rooting depth distribution and nitrogen acquisition by dominant tundra plant species. In preparation.

Smith LC, Sheng Y, MacDonald GM, Hinzman LD. 2005. Disappearing Arctic lakes. Science 308: 1429-1429.

Smith NG, Dukes JS. 2013. Plant respiration and photosynthesis in global-scale models: incorporating acclimation to temperature and CO2. Global Change Biology 19: 45-63.

Song C, Xu X, Sun X, Tian H, Sun L, Miao Y, Wang X, Guo Y. 2012. Large methane emission upon spring thaw from natural wetlands in the northern permafrost region. Environmental Research Letters 7: 034009.

Soudzilovskaia NA, van Bodegom PM, Cornelissen JHC. 2013. Dominant bryophyte control over high-latitude soil temperature fluctuations predicted by heat transfer traits, field moisture regime and laws of thermal insulation. Functional Ecology 27: 1442-1454.


Steinweg JM, Dukes JS, Wallenstein MD. 2012. Modeling the effects of temperature and moisture on soil enzyme activity: Linking laboratory assays to continuous field data. Soil Biology and Biochemistry 55: 85-92.

Sturm M, Racine C, Tape K. 2001a. Climate change: increasing shrub abundance in the Arctic. Nature 411(6837): 546-547.

Sturm M, Holmgren J, McFadden JP, Liston GE, Chapin FS III, Racine CH. 2001b. Snow-shrub interactions in Arctic tundra: a hypothesis with climatic implications. Journal of Climate 14(3): 336-344.

Sturm M, Douglas T, Racine C, Liston GE. 2005. Changing snow and shrub conditions affect albedo with global implications. Journal of Geophysical Research 110: G01004.

Sturtevant CS, Oechel WC, Zona D, Kim Y, Emerson CE. 2012. Soil moisture control over autumn season methane flux, Arctic Coastal Plain of Alaska. Biogeosciences 9: 1423-1440.

Subin ZM, Murphy LN, Li FY, Bonfils C, Riley WJ. 2012. Boreal lakes moderate seasonal and diurnal temperature variation and perturb atmospheric circulation: analyses in the Community Earth System Model 1 (CESM1). Tellus A 64, ISI:000300396900001.

Subin ZM, Koven CD, Riley WJ, Torn MS, Lawrence DM, Swenson SC. 2013. Effects of soil moisture on the responses of soil temperatures to climate change in cold regions. Journal of Climate 26: 3139-3158.

Swann AL, Fung IY, Levis S, Bonan GB, Doney SC. 2010. Changes in Arctic vegetation amplify high-latitude warming through the greenhouse effect. Proceedings of the National Academy of Sciences USA 107: 1295–1300. doi:10.1073/pnas.0913846107.

Swanson, DK. 2010. Mapping of Erosion Features Related to Thaw of Permafrost in Bering Land Bridge National Preserve, Cape Krusenstern National Monument, and Kobuk Valley National Park. Natural Resource Data Series NPS/ARCN/NRDS—2010/122. National Park Service, Fort Collins, Colorado.

Swanson, DK. 2013. Three decades of landscape change in Alaska's arctic national parks. Page 42 in Natural Resource Technical Report. National Park Service, Department of the Interior, Fort Collins, CO.

Swenson SC, Lawrence DM, Lee H. 2012. Improved simulation of the terrestrial hydrological cycle in permafrost regions by the Community Land Model. Journal of Advances in Modeling Earth Systems 4.

Tang JY, Riley WJ. 2013. A total quasi-steady-state formulation of substrate uptake kinetics in complex networks and an example application to microbial litter decomposition. Biogeosciences 10: 8329-8351. doi:10.5194/bg-10-8329-2013.

Tang J, Riley WJ. 2015. Weaker soil carbon-climate feedbacks resulting from microbial and abiotic interactions. Nature Climate Change 5: 56-60. doi:10.1038/NCLIMATE2438.

Tang G, Hwang T, Pradhanang S. 2014. Does consideration of water routing affect simulated water and carbon dynamics in terrestrial ecosystems? Hydrology and Earth System Sciences 18: 1423-1437.

Tape, KEN, Sturm M, Racine C. 2006. The evidence for shrub expansion in northern Alaska and the Pan‐Arctic. Global Change Biology 12(4): 686-702.

Tesfa TK, Ruby Leung L, Huang M, Li HY, Voisin N, Wigmosta MS. 2014. Scalability of grid‐and subbasin‐based land surface modeling approaches for hydrologic simulations. Journal of Geophysical Research: Atmospheres 119: 3166-3184.

http://dx.doi.org/10.5194/bg-10-8329-2013

http://dx.doi.org/10.5194/bg-10-8329-2013

http://dx.doi.org/10.1038/NCLIMATE2438



Thomas, RB, Van Bloem SJ, Schlesinger WH, Newton P, Carran R, Edwards G, Niklaus P. 2007. Climate change and symbiotic nitrogen fixation in agroecosystems. Agroecosystems in a Changing Climate 85-116.

Thornton PE, Zimmermann NE. 2007. An improved canopy integration scheme for a land surface model with prognostic canopy structure. Journal of Climate 20(15): 3902-3923. doi:10.1175/JCLI4222.1.

Treat CC, Natali SM, Ernakovich J, Iversen CM, Lupascu M, McGuire AD, Norby RJ, Roy Chowdhury T, Richter A, Šantrůčková H, Schädel C, Schuur EAG, Sloan VL, Turetsky MR, Waldrop MP. 2015. A pan-Arctic synthesis of CH4 and CO2 production from anoxic soil incubations. Global Change Biology. doi:10.1111/gcb.12875.

Tuller M, Or D. 2005. Water retention and soil water characteristics curve. Pages 278-284 in Encyclopedia of Soils in the Environment. Elsevier Science.

van Bodegom, PM, Douma JC, Verheijen LM. 2014. A fully traits-based approach to modeling global vegetation distribution. Proceedings of the National Academy of Science USA 111: 13733–13738.

Vázquez-Ortega A, Hernandez-Ruiz S, Amistadi MK, Rasmussen C, Chorover J. 2014. Fractionation of dissolved organic matter by (oxy)hydroxide-coated sands: competitive sorbate displacement during reactive transport. Vadose Zone Journal 13.

Vogel, J, Schuur EAG, Trucco C, Lee H. 2009. Response of CO2 exchange in a tussock tundra ecosystem to permafrost thaw and thermokarst development. Journal of Geophysical Research 114: G04018.

Voisin N, Li H, Ward D, Huang M, Wigmosta M, & Leung L. 2013. On an improved sub-regional water resources management representation for integration into earth system models. Hydrology and Earth System Sciences 17: 3605-3622.

Wainwright HM, Dafflon B, Smith LJ, Hahn MS, Curtis JB, Wu Y, Ulrich C, Peterson JE, Torn MS, Hubbard SS. 2015. Identifying multiscale zonation and assessing the relative importance of polygon geomorphology on carbon fluxes in an Arctic tundra ecosystem. JGR Biogeosciences. In press. doi:10.1002/2014JG002799.

Wallenstein MD, Weintraub MN. 2008. Emerging tools for measuring and modeling the in situ activity of soil extracellular enzymes. Soil Biology and Biochemistry 40: 2098-2106.

Wallenstein MD, McMahon SK, Schimel JP. 2009. Seasonal variation in enzyme activities and temperature sensitivities in Arctic tundra soils. Global Change Biology 15: 1631-1639.

Walsh JE, Anisimov O, Hagen JOM, et al. 2005. Chapter 6, Cryosphere and Hydrology. In Arctic Climate Impact Assessment, C Symon (ed). Cambridge University Press, New York.

Walvoord MA, Striegl RG. 2007. Increased groundwater to stream discharge from permafrost thawing in the Yukon River basin: Potential impacts on lateral export of carbon and nitrogen. Geophysical Research Letters 34.

Wang G, Post WM, Mayes MA. 2012. Development of microbial-enzyme-mediated decomposition model parameters through steady-state and dynamic analyses. Ecological Applications 23: 255-272.

Wang D, Xu Y, Thornton PE, King AW, Gu L, Steed C, Schuchart J. 2014a. A functional testing platform for the Community Land Model. Environmental Modeling and Software 55: 25-31. doi:10.1016/j.envsoft.2014.01.015.

http://dx.doi.org/10.1002/2014JG002799


Wang W, Xiao J, Ollinger SV, Desai AR, Chen J, Noormets A. 2014b. Quantifying the effects of harvesting on carbon fluxes and stocks in northern temperate forests. Biogeosciences 11(23): 6667-6682. doi:10.5194/bg-11-6667-2014.

Weintraub MN, Schimel JP. 2005. Nitrogen cycling and the spread of shrubs control changes in the carbon balance of Arctic tundra ecosystems. BioScience 55(5): 408-415. doi:10.1641/0006-3568(2005)055[0408:NCATSO]2.0.CO;2.

Whalley WR, Ober ES, Jenkins M. 2013. Measurement of the matric potential of soil water in the rhizosphere. Journal of Experimental Botany 64: 3951-3963.

Winton, M. 2006. Amplified Arctic climate change: what does surface albedo feedback have to do with it? Geophysical Research Letters 33(3). doi:10.1029/2005GL025244.

Wolfe BB, Light EM, Macrae ML, et al. 2011. Divergent hydrological responses to 20th century climate change in shallow tundra ponds, western Hudson Bay Lowlands. Geophysical Research Letters 38.

Wookey PA, Aerts R, Bardgett RD, et al. 2009. Ecosystem feedbacks and cascade processes: understanding their role in the responses of Arctic and alpine ecosystems to environmental change. Global Change Biology 15: 1153–1172.

Wullschleger SD, Epstein HE, Box EO, Euskirchen ES, Goswami S, Iversen CM, Kattge J, Norby RJ, van Bodegom PM, Xu X. 2014. Plant functional types in earth system models: past experiences and future directions for application of dynamic vegetation models in high-latitude ecosystems. Annals of Botany 114: 1-16. doi:10.1093/aob/mcu077.

Xu C, Fisher R, Wullschleger SD, Wilson CJ, Cai M, McDowell NG. 2012. Toward a mechanistic modeling of nitrogen limitation on vegetation dynamics. PLoS ONE 7(5): e37914. doi:10.1371/journal.pone.0037914.

Zhang X, He J, Zhang J, Polyakov I, Gerdes R, Inoue J, Wu P. 2013. Enhanced poleward moisture transport and amplified northern high-latitude wetting trend. Nature Climate Change 3: 47-51.

Zhang Y, Olthof I, Fraser R, Wolfe SA. 2014. A new approach to mapping permafrost and change incorporating uncertainties in ground conditions and climate projections. The Cryosphere 8: 2177-2194.

Zhu Q, Riley WJ. 2015. Improved modeling of soil nitrogen losses. Nature Climate Change. Accepted.

Zhu Q, Riley WJ, Tang J, Koven CD. 2015. Multiple soil nutrient competition between plants, microbes, and mineral surfaces: model development, parameterization, and example applications in several tropical forests. Biogeosciences Discuss. 12: 4057–4106.

Zimmerman AR, Ahn M-Y. 2011. Organo-mineral–enzyme interaction and soil enzyme activity. Pages 271-292 in G Shukla, A Varma (eds), Soil Enzymology. Springer-Verlag, Berlin.

Zimov SA, Davydov SP, Zimova GM, Davydova AI, Schuur EAG, Dutta K, Chapin FS III. 2006. Permafrost carbon: stock and decomposability of a globally significant carbon pool. Geophys. Res. Lett. 33: L20502.

http://dx.doi.org/10.1093/aob/mcu077


APPENDIX P a g e | A-1

APPENDIX Project Publications ............................................................................................................................................................ A‐3 Project Roles, Responsibilities, and Authorities ..................................................................................................... A‐7 Data Management Plan ..................................................................................................................................................... A‐9 Data Sharing Policy .......................................................................................................................................................... A‐11 Model Sharing Policy ....................................................................................................................................................... A‐17 Fair Use Policy ................................................................................................................................................................... A‐19 Selected Dataset DOIs and Citations ......................................................................................................................... A‐21 Project Work Flow Diagram ......................................................................................................................................... A‐23


This Page Intentionally Left Blank


PROJECT PUBLICATIONS Hanson PJ, KE Childs, SD Wullschleger, JS Riggs, WK Thomas, DE Todd and JM Warren. 2010. A method for experimental heating of intact soil profiles for application to climate change experiments. Global Change Biology 17: 1083-1096. http://dx.doi.org/10.1111/j.1365-2486.2010.02221.x

Rowland JC, CE Jones, G Altmann, R Bryan, BT Crosby, LD Hinzman, DL Kane, DM Lawrence, A Mancino , P Marsh, JP McNamara, VE Romanovsky, H Toniolo, BJ Travis, E Trochim, CJ Wilson and GL Geernaert. 2010. Arctic Landscapes in Transition: Responses to Thawing Permafrost. EOS, Transactions American Geophysical Union 91: 229-230. http://dx.doi.org/10.1029/2010EO260001

Wullschleger SD and M Strahl. 2010. Climate Change: A Controlled Experiment. Scientific American 302: 78-83. http://dx.doi.org/10.1038/scientificamerican0310-78

Frampton A, S Painter, SW Lyon and G Destouni. 2011. Non-isothermal, three-phase simulations of near-surface flows in a model permafrost system under seasonal variability and climate change. Journal of Hydrology 403: 352-359. http://dx.doi.org/10.1007/s10040-012-0938-z

Koven CD, B Ringeval, P Friedlingstein, P Ciais, P Cadule, D Khvorostyanov, G Krinner and C Tarnocai. 2011. Permafrost carbon-climate feedbacks accelerate global warming. Proceedings of the National Academy of Sciences 108: 14769-14774. http://dx.doi.org/10.1073/pnas.1103910108

Rowland JC, B J Travis and CJ Wilson. 2011. The role of advective heat transport in talik development beneath lakes and ponds in discontinuous permafrost. Geophysical Research Letters 38: L17504. http://dx.doi.org/10.1029/2011GL048497

Wullschleger SD, LD Hinzman and CJ Wilson. 2011. Planning the next generation of Arctic Ecosystem Experiments. EOS, Transactions American Geophysical Union 92: 145. http://dx.doi.org/10.1029/2011EO170006

Xu C, C Liang, SD Wullschleger, CJ Wilson and NG McDowell. 2011. Importance of feedback loop between soil inorganic nitrogen and microbial community in the heterotrophic soil respiration response to global warming. Nature Reviews Microbiology 9: 222-223. http://dx.doi.org/10.1038/nrmicro2439-c1

Bouskill NJ, J Tang, WJ Riley and EL Brodie. 2012. Trait-based representation of biological nitrification: model development, testing, and predicted community composition. Frontiers in Microbiology 3: Article 364. http://dx.doi.org/10.3389/fmicb.2012.00364

Graham DE, MD Wallenstein, TA Vishnivetskaya, MP Waldrop, TJ Phelps, SM Pfiffner, TC Onstott, LG Whyte, D Gilichinsky, DA Elias, R Mackelprang, NC Verberkmoes, RL Hettich, D Wagner, SD Wullschleger and JK Jansson. 2012. Microbes in thawing permafrost: The unknown variable in the climate change equation. The ISME Journal 6: 709-712. http://dx.doi.org/10.1038/ismej.2011.163

Lee H, SD Wullschleger and Y Luo. 2012. Enhancing terrestrial ecosystem sciences by integrating empirical modeling approaches. EOS 93: 237. http://dx.doi.org/10.1029/2012EO250008

Lewis KC, Zyvoloski GA, Travis B, C Wilson and J Rowland. 2012. Drainage subsidence associated with Arctic permafrost degradation. Journal of Geophysical Research 117: Article F04019. http://dx.doi.org/10.1029/2011JF002284

McCarthy HR, Y Luo and SD Wullschleger. 2012. Integrating empirical-modeling approaches to improve understanding of terrestrial ecology processes. New Phytologist 195: 523-525. http://dx.doi.org/10.1111/j.1469-8137.2012.04222.x

McGuire AD, Christensen TR, Hayes D, Heroult A, Euskirchen E, Kimball JS, Koven C, Lafleur P, Miller PA, Oechel W, Peyton P, Williams M and Yi Y. 2012. An assessment of the carbon balance of Arctic tundra: comparisons among observations, process models, and atmospheric inversions. Biogeosciences. 9: 3185-3204. http://dx.doi.org/10.5194/bg-9-3185-2012








http://dx.doi.org/10.1111/j.1365-2486.2010.02221.x









http://ngee-arctic.ornl.gov/content/arctic-landscapes-transition-responses-thawing-permafrost-0

http://dx.doi.org/10.1029/2010EO260001



http://dx.doi.org/10.1038/scientificamerican0310-78





http://ngee-arctic.ornl.gov/content/non-isothermal-three-phase-simulations-near-surface-flows-model-permafrost-system-under-seas

http://ngee-arctic.ornl.gov/content/non-isothermal-three-phase-simulations-near-surface-flows-model-permafrost-system-under-seas

http://dx.doi.org/10.1007/s10040-012-0938-z









http://dx.doi.org/10.1029/2011GL048497

http://dx.doi.org/10.1029/2011EO170006

http://dx.doi.org/10.1038/nrmicro2439-c1

http://dx.doi.org/10.3389/fmicb.2012.00364

http://dx.doi.org/10.1038/ismej.2011.163

http://dx.doi.org/10.1029/2012EO250008

http://dx.doi.org/10.1029/2011JF002284

http://dx.doi.org/10.1111/j.1469-8137.2012.04222.x











http://dx.doi.org/10.5194/bg-9-3185-2012


Xu C, R. Fisher, SD Wullschleger, CJ Wilson, M Cai and NG McDowell. 2012. Toward a mechanistic modeling of nitrogen limitation on vegetation dynamics. PLoSONE e37914. http://dx.doi.org/10.1371/journal.pone.0037914

Cunningham P, RR Linn, E Koo and CJ Wilson. 2013. Large-eddy simulations of air flow and turbulence within and around low-aspect-ratio cylindrical open-top chambers. Journal of Applied Meteorology and Climatology 52: 1716-1737. http://dx.doi.org/10.1175/JAMC-D-12-041.1

Dafflon B, SS Hubbard, C Ulrich and JE Peterson. 2013. Electrical conductivity imaging of active layer and permafrost in an Arctic ecosystem through advanced inversion of electromagnetic induction data. Vadose Zone Journal 12: Article 4. http://dx.doi.org/10.2136/vzj2012.0161

Hinzman LH, G Destouni and M-k Woo. 2013. Preface: Hydrology of cold regions. Hydrogeology Journal 21: 1-4. http://dx.doi.org/10.1007/s10040-012-0943-2

Hinzman LD, CJ Deal, AD McGuire, SH Mernild, IV Polyakov and JE Walsh. 2013. Trajectory of the Arctic as an integrated system. Ecological Applications 23: 1837-1868. http://dx.doi.org/10.1890/11-1498.1

Hoffman FM, J Kumar, RT Mills and WW Hargrove. 2013. Representativeness-based sampling network design for the State of Alaska. Landscape Ecology 28: 1567-1586. http://dx.doi.org/10.1007/s10980-013-9902-0

Hubbard SS, C Gangodagamage, B Dafflon, H Wainwright, J Peterson, A Gusmeroli, C Ulrich, Y Wu, C Wilson, J Rowland, C Tweedie and SD Wullschleger. 2013. Quantifying and relating subsurface and land-surface variability in permafrost environments using surface geophysical and LiDAR datasets. Hydrogeology 21: 149-169. http://dx.doi.org/10.1007/s10040-012-0939-y

Painter SL, JD Moulton and CJ Wilson. 2013. Modeling challenges for predicting hydrologic response to degrading permafrost. Hydrogeology 21: 221-224. http://dx.doi.org/10.1007/s10040-012-0917-4

Riley WJ. 2013. Using model reduction to predict the soil-surface C18OO flux: an example of representing complex biogeochemical dynamics in a computationally efficient manner. Geoscientific Model Development 6: 345-352. http://dx.doi.org/10.5194/gmd-6-345-2013

Shurikhin AN, C Gangodagamage, JC Rowland and CJ Wilson. 2013. Arctic tundra ice-wedge landscape characterization by active contours without edges and structural analysis using high-resolution satellite imagery. Remote Sensing Letters 4: 4: 1077-1086. http://dx.doi.org/10.1080/2150704X.2013.840404

Tang JY and WJ Riley. 2013. A total quasi-steady-state formulation of substrate uptake kinetics in complex networks and an example application to microbial litter decomposition. Biogeosciences 10: 8329-8351. http://dx.doi.org/10.5194/bg-10-8329-2013

Wu Y, SS Hubbard, C Ulrich and SD Wullschleger. 2013. Remote monitoring of freeze-thaw transitions in Arctic soils using the complex resistivity method. Vadose Zone Journal 12: 1. http://dx.doi.org/10.2136/vzj2012.0062

Bouskill NJ, WJ Riley and JY Tang. 2014. Meta-analysis of high-latitude nitrogen-addition and warming studies implies ecological mechanisms overlooked by land models. Biogeosciences 11: 6969-6983. http://dx.doi.org/10.5194/bg-11-6969-2014

Dou S and J Ajo-Franklin. 2014. Full-wavefield inversion of surface waves for mapping embedded low-velocity zones in permafrost. Geophysics 79: EN107-EN124. http://dx.doi.org/10.1190/GEO2013-0427.1

Gangodagamage C, JC Rowland, SS Hubbard, SP Brumby, AK Liljedahl, H Wainwright, CJ Wilson, GL Altmann, B Dafflon, J Peterson, C Ulrich, CE Tweedie and SD Wullschleger. 2014. Extrapolating active layer thickness measurements across Arctic polygonal terrain using LiDAR and NDVI data sets. Water Resources Research 50: 6339-6357. http://dx.doi.org/10.1002/2013WR014283

http://dx.doi.org/10.1371/journal.pone.0037914

http://dx.doi.org/10.1175/JAMC-D-12-041.1

http://dx.doi.org/10.2136/vzj2012.0161

http://dx.doi.org/10.1007/s10040-012-0943-2

http://dx.doi.org/10.1890/11-1498.1

http://dx.doi.org/10.1890/11-1498.1

http://dx.doi.org/10.1007/s10980-013-9902-0

http://dx.doi.org/10.1007/s10980-013-9902-0

http://dx.doi.org/10.1007/s10040-012-0939-y

http://dx.doi.org/10.1007/s10040-012-0917-4


http://dx.doi.org/10.1080/2150704X.2013.840404

http://dx.doi.org/10.5194/bg-10-8329-2013

http://dx.doi.org/10.2136/vzj2012.0062

http://dx.doi.org/10.5194/bg-11-6969-2014

http://dx.doi.org/10.1190/GEO2013-0427.1

http://dx.doi.org/10.1002/2013WR014283


Hayes DJ, DW Kicklighter, AD McGuire, M Chen, Q Zhuang, F Yuan, JM Melillo and SD Wullschleger. 2014. The impacts of recent permafrost thaw on land-atmosphere greenhouse gas exchange. Environmental Research Letters 9: Article 045005. http://dx.doi.org/10.1088/1748-9326/9/4/045005

Jansson JK and N Tas. 2014. The microbial ecology of permafrost. Nature Reviews Microbiology. http://dx.doi.org/10.1038/nrmicro3262

Karra S, SL Painter and PC Lichtner. 2014. Three-phase numerical model for subsurface hydrology in permafrost-affected regions (PFLOTRAN-ICE v1.0). The Cryosphere Discussion 8: 1935-1950. http://dx.doi.org/10.5194/tc-8-1935-2014

Moody DI, SP Brumby, JC Rowland and GL Altmann. 2014. Land cover classification in multispectral imagery using clustering of sparse approximations over learned feature dictionaries. Journal of Applied Remote Sensing 8: 084793-1. http://dx.doi.org/10.1117/1.JRS.8.084793.

Painter SL and S Karra. 2014. Constitutive model for unfrozen water content in subfreezing unsaturated soils. Vadose Zone Journal, Volume 13, Article 4. http://dx.doi.org/10.2136/vzj2013.04.0071

Pau GSH, G Bisht and WJ Riley. 2014. A reduced-order modeling approach to represent subgrid-scale hydrological dynamics for land-surface simulations: Application in a polygonal tundra landscape. Geoscientific Model Development 7: 2091-2105. http://dx.doi.org/10.5194/gmd-7-2091-2014

Riley WJ, FM Maggi, M Kleber, MS Torn, JY Tang, D Dwivedi and N Guerry. 2014. Long residence times of rapidly decomposable soil organic matter: Application of, a multi-phase, multi-component, and vertically-resolved model (BAMS1) to soil carbon dynamics. Geoscientific Model Development 7: 1335-1355. http://dx.doi.org/10.5194/gmd-7-1335-2014

Riley WJ and C Shen. 2014. Characterizing coarse-resolution watershed soil moisture heterogeneity using fine-scale simulations and reduced-order models. Hydrology and Earth System Science 18: 2463-2483. http://dx.doi.org/10.5194/hess-18-2463-2014

Rogers A. 2014. The use and misuse of Vcmax in Earth System Models. Photosynthesis Research 119: 15029. http://dx.doi.org/10.1007/s11120-013-9818-1

Rogers A, BE Medlyn and JS Dukes. 2014. Improving representation of photosynthesis in Earth System Models. New Phytologist 204: 12-14. http://dx.doi.org/10.1111/nph.12972

Tang J and WJ Riley. 2014. Technical Note: Simple formulations and solutions of the dual-phase diffusive transport for biogeochemical modeling. Biogeosciences Discussion 11: 1587-1611. http://dx.doi.org/10.5194/bgd-11-1587-2014

Wullschleger SD, HE Epstein, EO Box, ES Euskirchen, S Goswami, CM Iversen, J Kattge, RJ Norby, PM van Bodegom and X Xu. 2014. Plant functional types in Earth System Models: Past experiences and future directions for application of dynamic vegetation models in high-latitude ecosystems. Annals of Botany 114: 1-16. http://dx.doi.org/10.1093/aob/mcu077

Chowdhury TR, EM Herndon, TJ Phelps, DA Elias, B Gu, L Liang, SD Wullschleger and DE Graham. 2014. Stoichiometry and temperature sensitivity of methanogenesis and CO2 production from saturated polygon tundra in Barrow, Alaska. Global Change Biology 21: 722-737. http://dx.doi.org/10.1111/gcb.12762

Cohen LR, N Raz-Yaseef, JB Curtis, JM Young, TA Rahn, CJ Wilson, SD Wullschleger and BD Newman. 2014. Measuring diurnal cycles of evapotranspiration in the Arctic with an automated chamber system. Ecohydrology (in press). http://dx.doi.org/10.1002/eco.1532

Heikoop, J.M., H.M. Throckmorton, B.D. Newman, G.B. Perkins, C.M. Iversen, T. Roy Chowdhury, V. Romanovsky, D.E. Graham, R.J. Norby, C.J. Wilson, and S.D. Wullschleger. 2015. Isotopic identification of soil and permafrost nitrate sources in an Arctic tundra ecosystem. JGR Biogeosciences (accepted)

http://dx.doi.org/10.1088/1748-9326/9/4/045005

http://dx.doi.org/10.1038/nrmicro3262

http://dx.doi.org/10.5194/tc-8-1935-2014

http://dx.doi.org/10.1117/1.JRS.8.084793

http://dx.doi.org/10.2136/vzj2013.04.0071



http://dx.doi.org/10.5194/hess-18-2463-2014

http://dx.doi.org/10.1007/s11120-013-9818-1


http://dx.doi.org/10.5194/bgd-11-1587-2014



http://dx.doi.org/10.1002/eco.1532


Iversen CM, VL Sloan, PF Sullivan, E Euskirchen, AD McGuire, RJ Norby, AP Walker, JM Warren and SD Wullschleger. 2015. The unseen iceberg: plant roots in arctic tundra. New Phytologist 205: 34-58. http://dx.doi.org/10.1111/nph.13003

Koven CD, DM Lawrence and WJ Riley. 2015. Permafrost carbon-climate feedback is sensitive to deep soil carbon decomposability but not deep soil nitrogen dynamics. Proceedings of the National Academy of Sciences USA. http://dx.doi.org/10.1073/pnas.1415123112

Lara MJ, AD McGuire, ES Euskirchen, CE Tweedie, KM Hinkel, AN Skurikhin, VE Romanovsky, G Grosse, WR Bolton and H Genet. 2015. Polygonal tundra geomorphological change in response to warming alters future CO2 and CH4 flux on the Barrow Peninsula. Global Change Biology 21: 1634-1651. http://dx.doi.org/10.1111/gcb.12757

Lin et al. 2015. Optimal stomatal behaviour around the world. Nature Climate Change 5: 459-464. http://dx.doi.org/10.1038/nclimate2550

Maggi FM and W. Riley. 2015. The effect of temperature on the rate, affinity, and 15N fractionation of NO3− during biological denitrification in soils. Biogeochemistry. http://dx.doi.org/10.1007/s10533-015-0095-2

Muskett RR, VE Romanovsky, WL Cable and AL Kholodov. 2015. Active-layer soil moisture content regional variations in Alaska and Russia by ground-based and satellite-based methods, 2002 through 2014. International Journal of Geosciences 6: 12-41. http://dx.doi.org/10.4236/ijg.2015.61002

Newman BD, HM Throckmorton, DE Graham, B Gu, SS Hubbard, L Liang, Y Wu, JM Heikoop, EM Herndon, TJ Phelps, CJ Wilson and SD Wullschleger. 2015. Microtopographic and depth controls on active layer chemistry in Arctic polygonal ground. Geophysical Research Letters 42: 1808-1817. http://dx.doi.org/10.1002/2014GL062804

Schuur EAG, AD McGuire, C Schädel, G. Grosse, J.W. Harden, D.J. Hayes, G. Hugelius, CD Koven, P Kuhry, DM Lawrence, SM Natali, D Olefeldt, VE Romanovsky, K Schaefer, MR Turetsky, CC Treat and JE Vonk. 2015. Climate change and the permafrost carbon feedback. Nature 520: 171-179. http://dx.doi.org/10.1038/nature14338

Tang J and WJ Riley. 2015. Weaker soil carbon-climate feedbacks resulting from microbial and abiotic interactions. Nature Climate Change 5: 56-60. http://dx.doi.org/10.1038/NCLIMATE2438

Wainwright HM, B Dafflon, LJ Smith, MS Hahn, JB Curtis, Y Wu, C Ulrich, JE Peterson, MS Torn and SS Hubbard. 2015. Identifying multiscale zonation and assessing the relative importance of polygon geomorphology on carbon fluxes in an Arctic tundra ecosystem. JGR Biogeosciences (in press). http://dx.doi.org/10.1002/2014JG002799

Warren JM, PJ Hanson, CM Iversen, J Kumar, AP Walker and SD Wullschleger. 2015. Root structural and functional dynamics in terrestrial biosphere models – Evaluation and recommendations. New Phytologist 205: 59-78. http://dx.doi.org/10.1111/nph.13034

Weston DJ, C Timm, A Walker, L Gu, W Muchero, J Schmutz, JA Shaw, GA Tuskan, J Warren and SD Wullschleger. 2015. Sphagnum physiology in the context of changing climate: Emergent influences of genomics and host-microbiome interactions to ecosystem function. Plant, Cell and Environment (in press). http://dx.doi.org/10.1111/pce.12458

Wullschleger SD, JM Warren and PE Thornton. 2015a. Leaf respiration (GlobResp) – Global trait database supports Earth System Models. New Phytologist 206: 483-485. http://dx.doi.org/10.1111/nph.13364

Wullschleger SD, AL Breen, CM Iversen, MS Olson, T Nasholm, U Ganeteg, MD Wallenstein and DJ Weston. 2015b. Genomics in a changing Arctic: Critical questions await the molecular ecologist. Molecular Ecology 24: 2301-2309. http://dx.doi.org/10.1111/mec.13166




http://dx.doi.org/10.1038/nclimate2550

http://dx.doi.org/10.1007/s10533-015-0095-2

http://dx.doi.org/10.1007/s10533-015-0095-2

http://dx.doi.org/10.4236/ijg.2015.61002

http://dx.doi.org/10.1002/2014GL062804

http://dx.doi.org/10.1038/nature14338


http://dx.doi.org/10.1002/2014JG002799


http://dx.doi.org/10.1111/pce.12458


http://dx.doi.org/10.1111/mec.13166


PROJECT ROLES, RESPONSIBILITIES, AND AUTHORITIES Table A-1. NGEE Arctic project personnel’s roles, responsibilities, and authorities

Role Responsibilities Authorities Science Advisory Board

● Advise on the scientific thrusts of the project ● Review project plans ● Review progress toward project goals

● Assess performance of the project R&D team ● Assess scientific quality and discuss progress with project director and chief scientist

Laboratory Research Director

● Provide overall leadership for the NGEE Arctic project ● Single contact point for DOE ● Ensure project integration ● Seek inputs from the core team; data, operations and finance managers ● Capability development

● Exercise full authority to manage all aspects of the project with DOE approval; approve yearly program plan and release budget; make requests to STL’s, project manager for regular milestone/deliverable and finance manager for financial report documentation; data manager for input/ reports

Chief Scientist ● Contribute to scientific direction of the project ● Establish connections to national and international scientific community

● Represent NGEE Arctic project goals and objectives to larger Arctic science community ● Seek out collaborations on behalf of the project

Institutional Lead

● Advise LRD ● Track institutional budgets against deliverables ● Assist with planning and reviews ● Anticipate staffing issues and resolution of performance concerns

● Coordinate development of institutional task plan and budgets ● Monitor institutional deliverables across science areas ● Plan adjustments to project plan and budget allocations as appropriate

Science Team Leader

● Plan EOMIs to integrate activities within and across the project elements ● Monitor deliverables and progress planned. ● Conduct periodic reviews of their plan and make adjustments via the change control plan. ● Track budgets against EOMIs and deliverables ● Provide inputs for periodic reports ● Mentor staff and facilitate collaboration

● Set objectives and deliverables for their focus area ● Develop multi-year plans with EOMIs and Key Task to attain deliverables (WBS) ● Build and review budgets based on EOMI rollup ● Monitor progress and meet financial performance targets ● Assess subcontractor performance

Key Task Leader

● Plan tasks to complete assigned Key Tasks as assigned by STL ● Execute Key Tasks

● Develop work plans especially for Field Work ● Consult with institutional safety support when planning work with new hazards.

Participants ● Execute scope of research consistent with proposal plan. ● Investigators are responsible for data collection, documentation, upload and release. ● Modelers are responsible for planning and modifying code, documenting, and uploading and

● Modify scope of work as appropriate in consultation with TL and/or STL ● Alert appropriate STL or LRD when problems arise


releasing ● All are responsible for record keeping, analysis, interpretation, and submission of required reports and publications

Project Manager

● Gather dashboard data and share with project participants ● Generate regular reports ● Monitor deliverables ● Subcontractor management ● Provide financial management and reporting to project director, chief scientist, and science leaders ● Responsible for ESH&Q

● Manage planning documents including project time lines and work breakdown structure (WBS) ● Request project information as requested by the Core Team and report to project director ● Request input from subcontractors for LRD and STL ● Assess research safety and quality plans

Data Manager ● Develop a data management strategy based on the resources allocated by LRD ● Execute the data management practices ● Seek input from QA manager for initial QA/quality control for data collected ● Direct Data Representatives

● Request data status reports from STLs and data inputs from science teams with approved data reporting and archival procedures ● Raise issues to metadata contact, STL, TL, or Data Representative if and when a data quality problem arises

Data Representative

● Serve as local data submission guidance support ● Monitor Project Module and Metadata Database for potential data products and upcoming submission dates

● Liaison between local project staff and central DMT ● Raises questions with appropriate project staff when metadata records have problems, submission dates are missed and data issues arise ● Consult data manager when problems arise

Officer of the Day

● Ensure that daily meeting occurs, everyone has a buddy, and return times are known ● Gather weather information and bear reports. ● Serve as point of contact to ORNL and other institutions in the event of an accident.

● Halt field work when weather or other hazards that could jeopardize participants arise. ● Ensure that information is gathered from witnesses to any accident involving an NGEE participant in field locations


DATA MANAGEMENT PLAN

Data Types and Sources The Next-Generation Ecosystem Experiment (NGEE) Arctic project is composed of an array of researchers from multiple science disciplines across multiple national laboratories and universities. The NGEE Arctic project will continue to generate diverse data sets from observations, laboratory measurements, experiments, and models across field plot, watershed, regional, and global scales. These data will include automated data collected from weather stations and in situ systems, observations from remote-sensing platforms, manual data collection efforts during large campaign-based field work, and discrete data sets generated from chemical, biochemical, and molecular characterizations of soil, water, microbial, and plant samples. Large output files from a suite of fine- to climate-scale models will also be generated within the NGEE Arctic project. The project will draw on a wealth of existing data products collected and generated by other national and international monitoring networks and research organizations across the Arctic including the NASA funded CARVE and ABoVE projects.

Content and Format The NGEE Arctic project leverages existing tools and expertise to provide data management support to the project by adopting a standards-based, open-source approach to ensure interoperability with future NGEE systems and other projects. The ORNL Online Metadata Editor (OME) is a Web-based tool that allows users to create and maintain robust metadata stored as eXtensible Markup Language (XML) files, the preferred metadata output format, with output that conforms to and satisfies widely-adopted metadata standards – specifically Federal Geospatial Data Committee (FGDC) and ISO11915 metadata standards. The OME captures information about their specific project, parameters, time periods, quality assurance, and locations associated with the data. This metadata is the basis for the NGEE Arctic Search and Access Tool for data products.

Users may upload datasets plus additional documentation using the OME. The preferred non-proprietary file format for pubic sharing of tabular data products is the comma separated value format. For geospatial spatial imagery, GeoTIFF and NetCDF are the preferred formats for raster data and ARCVIEW shape files for vector products.

Sharing and Preservation All NGEE Arctic metadata records are available to the public in the NGEE Arctic Data Search and Access Tool. Associated datasets and documentation may have restricted access to NGEE Arctic Project Members only until made available for the public with unrestrictive access but with requested use recommendations. Descriptive information captured in the OME enables advanced data search options for NGEE data using keywords, geo-spatial, temporal and ontology filters. The search tool is accessible from the NGEE portal or with a direct link and provides access not only to NGEE Arctic collected data but also relevant data from other sources such as the Carbon Dioxide Information Analysis Center (CDIAC) and Atmospheric Radiation Measurement (ARM).

The NGEE Arctic portal provides data sharing policies (i.e., team sharing policies and a fair-use policy), data submission guidance, and data citation recommendations plus workflows for metadata review, data submission, and the generation of NGEE Arctic-specific Digital Object Identifiers (DOIs). Early sharing of data within the project is urged because it encourages vital scientific collaboration within the project, identifies planned research, allows access to the data as soon as possible for researchers and modelers, and ensures long-term preservation. Ideally, internal sharing will be three months from collection or completion of laboratory analysis with automated environmental measurements available following initial quality assurance. In practice, timelines for data submission will vary depending on data type, measurement, analysis, etc. so while meeting the ideal sharing timelines is a goal, the researchers will

http://ngee-arctic.ornl.gov/data

http://ngee-arctic.ornl.gov/data


have room to justify modifications. After passing quality assurance, data supporting publications will be publically available at the time of release in a publication or earlier at the discretion of the Science Team Lead.

Protection NGEE Arctic will not store personally identifiable or sensitive environmental information in its data system. If any are discovered, it will be removed. Intellectual property rights of investigators (for digital data) are protected by data system enforced access restrictions and promoted through data citation guidance and DOIs. Stored data are protected from loss due to system failures or inadvertent deletion by routine and test backup protocols.

Rationale The open sharing of NGEE Arctic data among project researchers, the broader scientific community, and the public is critical to meeting the scientific goals and objectives of the NGEE Arctic project and critical to advancing the mission of the Department of Energy (DOE), Office of Science, Biological and Environmental (BER) Terrestrial Ecosystem Science (TES) program where the strategic intent is to deliver quality scientific data and improved models regarding the potential effects of increasing greenhouse gas concentrations on the Earth's terrestrial biosphere and the role that terrestrial ecosystems play in the global carbon cycle.

Generated by the DMPTool on April 24, 2015 11:19


DATA SHARING POLICY


MODEL SHARING POLICY


FAIR USE POLICY


SELECTED DATASET DOIS AND CITATIONS Permafrost/active layer temperatures and micrometeorological stations Currently Internal Sharing

Romanovsky, V. and W. Cable. 2014. Subsurface Temperature, Moisture, Thermal Conductivity and Heat Flux, Barrow, Area A, B, C, D. NGEE Arctic Data Collection, Carbon Dioxide Information Analysis Center, Oak Ridge National Laboratory, U.S. Department of Energy, Oak Ridge, Tennessee, U.S.A. http://dx.doi.org/10.5440/1126515

Public Sharing Busey, B., V. Romanovsky, W. Cable and and L. Hinzman. 2014. Surface Meteorology, Barrow, Alaska, Area A, B, C and D, Ongoing from 2012. NGEE Arctic Data Collection, Carbon Dioxide Information Analysis Center, Oak Ridge National Laboratory, U.S. Department of Energy, Oak Ridge, Tennessee, U.S.A. http://dx.doi.org/10.5440/1164893 Busey, B., V. Romanovsky, W. Cable and and L. Hinzman. 2014. Continuous Snow Depth, Intensive Site 1, Barrow, Alaska. NGEE Arctic Data Collection, Carbon Dioxide Information Analysis Center, Oak Ridge National Laboratory, U.S. Department of Energy, Oak Ridge, Tennessee, U.S.A. http://dx.doi.org/10.5440/1163347

Geophysical monitoring system Currently Internal Sharing

Hubbard, S. and J. Peterson. 2015. Ground Penetrating Radar, Barrow, Alaska. NGEE Arctic Data Collection, Carbon Dioxide Information Analysis Center, Oak Ridge National Laboratory, U.S. Department of Energy, Oak Ridge, Tennessee, U.S.A. http://dx.doi.org/10.5440/1171723

Eddy covariance tower Currently Internal Sharing

Torn, M., D. Billesbach, and N. Raz-Yaseef. 2014. Eddy-Covariance and auxiliary measurements, NGEE-Barrow, 2012-2013. NGEE Arctic Data Collection, Carbon Dioxide Information Analysis Center, Oak Ridge National Laboratory, U.S. Department of Energy, Oak Ridge, Tennessee, U.S.A http://dx.doi.org/10.5440/1124200 Energy balance tram

Currently Internal Sharing Torn, M. and S. Serbin. 2015. BEO Tram Spectral Data 2014. NGEE Arctic Data Collection, Carbon Dioxide Information Analysis Center, Oak Ridge National Laboratory, U.S. Department of Energy, Oak Ridge, Tennessee, U.S.A. http://dx.doi.org/10.5440/1183993

Spatial array of water depth sensors for landscape hydrology Currently Internal Sharing

Liljedahl, A. and C. Wilson. 2015. Water Levels, Barrow, Alaska, NGEE Areas A, B, C and D for 2012, 2013, 2014, Final Version, 20150324. NGEE Arctic Data Collection, Carbon Dioxide Information Analysis Center, Oak Ridge National Laboratory, U.S. Department of Energy, Oak Ridge, Tennessee, U.S.A. http://dx.doi.org/10.5440/1183767

http://dx.doi.org/10.5440/1126515

http://dx.doi.org/10.5440/1164893

http://dx.doi.org/10.5440/1163347

http://dx.doi.org/10.5440/1171723

http://dx.doi.org/10.5440/1124200

http://dx.doi.org/10.5440/1183767


PROJECT WORK FLOW DIAGRAM

Figu

re A

-1. P

lann

ing

and

exec

utio

n of

res

earc

h an

d m

odel

ing

wor

k on

NG

EE

Arc

tic in

Pha

se 2

.


Figure A-2. EOMIs are the highest level activity in planning work. In the hierarchy of the EOMI are one or more Key Tasks that are assigned to team members for further planning. The Key Tasks are broken down into one or more actions called tasks. These tasks are ideally planned by the participants in each task. Planning should also include the expected output, for example, a field research task would return data or samples, they should be described.

Figure A-3. A key to successful and safe outcomes is good planning. Field work plans are a necessary element and can be uploaded via the PM and Data tools so they are available to team members.


Figure A-4. Planning for work in Alaska is complex and coordinating a team with proper equipment is vital to success.

Figure A-5. The new PM and Data toolsets will allow investigators to initiate their metadata records from the planning module to avoid entry of duplicate information.

This Page Intentionally Left Blank. NGEE Proposal... · Jitendra Kumar, Scott Painter, and Peter...

Documents

Transcript of This Page Intentionally Left Blank. NGEE Proposal... · Jitendra Kumar, Scott Painter, and Peter...